Reviewerly/exHarmony huggingface dataset:
Devising Interactive Theatre

This paper presents a case study of a long-term collaboration between a physical performance company and interactive digital artists. The collaboration has resulted in the creation of five major performance works which have toured internationally over several years. We argue that the interactive systems can be considered a 'material' which changes over time, shaping performer actions and being shaped by them in return. Based on detailed interviews with key stakeholders and our own personal reflections, we have identified several 'trajectories' that have evolved over the duration of each individual production and the entire body of work. These trajectories address a number of perspectives including the way performers interact with the system, the relationship between the dramaturgy and the interaction palette and the way the stakeholders conceive of the interactive system. The evolution of the technology itself has also been examined in terms of aesthetic capability, performance robustness, operational cost and complexity across the entire duration of the collaboration.
Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models

Scientific computational models are crucial for analyzing and understanding complex real-life systems that are otherwise difficult for experimentation. However, the complex behavior and the vast input-output space of these models often make them opaque, slowing the discovery of novel phenomena. In this work, we present HINT (Hessian INTerestingness) -- a new algorithm that can automatically and systematically explore black-box models and highlight local nonlinear interactions in the input-output space of the model. This tool aims to facilitate the discovery of interesting model behaviors that are unknown to the researchers. Using this simple yet powerful tool, we were able to correctly rank all pairwise interactions in known benchmark models and do so faster and with greater accuracy than state-of-the-art methods. We further applied HINT to existing computational neuroscience models, and were able to reproduce important scientific discoveries that were published years after the creation of those models. Finally, we ran HINT on two real-world models (in neuroscience and earth science) and found new behaviors of the model that were of value to domain experts.
A Free Energy Based Approach for Distance Metric Learning

We present a reformulation of the distance metric learning problem as a penalized optimization problem, with a penalty term corresponding to the von Neumann entropy of the distance metric. This formulation leads to a mapping to statistical mechanics such that the metric learning optimization problem becomes equivalent to free energy minimization. Correspondingly, our approach leads to an analytical solution of the optimization problem based on the Boltzmann distribution. The mapping established in this work suggests new approaches for dimensionality reduction and provides insights into determination of optimal parameters for the penalty term. Furthermore, we demonstrate that the metric projects the data onto direction of maximum dissimilarity with optimal and tunable separation between classes and thus the transformation can be used for high dimensional data visualization, classification, and clustering tasks. We benchmark our method against previous distance learning methods and provide an efficient implementation in an R package available to download at: \urlhttps://github.com/kouroshz/fenn
A Design Approach towards Affording the Trend of Privacy

Society is affected by the consequences of data collection, and there are trends visible in law, the public debate and technology that could make a privacy-conscious future possible. We study how to avoid data collection from the perspective and the role of design, to provide a starting point for new developments in this context. We do so by presenting a portfolio that exemplifies a range of possible design contributions. We show how to design smart products for retail and smart home while avoiding data collection, how to convince clients through design, and how to use design to spread awareness. We present design notions and reflections that stem from this portfolio for the synthesis of new designs, that further explore the potential of design in practice that affords the trend of privacy.
Implementation of Machine Learning Algorithms for Autonomous Robot Trajectory Resolving

Machine learning in the modern industry and transport became a hot issue and its implementation finding broad field in recent activities in industry and daily life. Robotic is widely using machine learning of different type for image recognizing and for robotic trajectory generation in unknown environment. This paper provides overview of existing feature and tries to predict trend of machine learning development in the robotics. Use of connection between units and machine learning features can improve intelligence and capabilities of robots for trajectory generation. Reviewed cases provides nice results, available on the field of robotic trajectory and brings some breakthrough movement towards fully autonomous devices. Finally, discussion and conclusions are provided in the paper.
Controlling Motion Blur in Synthetic Long Time Exposures

Abstract In a photo, motion blur can be used as an artistic style to convey motion and to direct attention. In panning or tracking shots, a moving object of interest is followed by the camera during a relatively long exposure. The goal is to get a blurred background while keeping the object sharp. Unfortunately, it can be difficult to impossible to precisely follow the object. Often, many attempts or specialized physical setups are needed. This paper presents a novel approach to create such images. For capturing, the user is only required to take a casually recorded hand‐held video that roughly follows the object. Our algorithm then produces a single image which simulates a stabilized long time exposure. This is achieved by first warping all frames such that the object of interest is aligned to a reference frame. Then, optical flow based frame interpolation is used to reduce ghosting artifacts from temporal undersampling. Finally, the frames are averaged to create the result. As our method avoids segmentation and requires little to no user interaction, even challenging sequences can be processed successfully. In addition, artistic control is available in a number of ways. The effect can also be applied to create videos with an exaggerated motion blur. Results are compared with previous methods and ground truth simulations. The effectiveness of our method is demonstrated by applying it to hundreds of datasets. The most interesting results are shown in the paper and in the supplemental material.
Micro-Architecture Optimization for Low-Power Bitcoin Mining ASICs

Cryptocurrencies have recently gained a lot of attention because their high security and easy transaction. Among the current cryptocurrencies, Bitcoin is the most well-known one. Application-specific ICs (ASICs) have been developed in order to deliver high throughput for Bitcoin mining. However, power dissipation is an important issue considering it causes increased mining cost and creates excessive heat. This paper presents three optimization techniques in the micro-architecture level for Bitcoin mining: deep pipelining, speculative computation, and approximate addition. The computations for Bitcoin mining are dominated by SHA-256, which can be realized by two-way 32-stage pipelines. Deep pipelining reduces the critical-path delay, resulting in less power due to architecture transformation and transistor sizing. The iterations of SHA-256 can be early terminated by leveraging speculative computation to prevent unnecessary switches. Approximate addition is adopted to reduce the critical-path delay of the compressor and expander at the cost of negligible precision loss. From the synthesis estimates at a 40-nm technology node, an overall 59.3% power reduction is achieved by applying these three techniques.
Camera calibration method of optical system for large field measurement of hot forgings in heavy industry

During the manufacturing process of heavy forgings, simple contact measuring techniques are still used to check the dimensions, therefore an optical measuring system is in demand. In this paper, a camera calibration method for the passive measuring system, which is being developed in collaboration with an industrial partner, is proposed. Our approach is based on space resection and works with robust coded targets, which are distributed in the field of view. The coordinates of targets are measured using TRITOP (GOM) measuring system. This solution allows to build a large calibration field, without a need of large calibration objects. The camera calibration works in 2 steps - at first, the intrinsic parameters of the camera, including lens distortion, are calibrated. These parameters are considered as stable, due to the use of special camera covers. Multi-image version of the calibration method and dense field of calibration targets are used. The second step is performed from every image and employs a single-image extrinsic camera parameters calibration method. Only a few coded calibration targets, mounted on stable objects in the scene, are required. The calibration method was tested in industrial conditions. The method showed great results, the average reprojection error was under 0.1 px. The effect of thermally affected zone on the calibration process is discussed.
An Improved Convolutional Neural Network Architecture for Image Classification

This manuscript presents the design and implementation of an improved convolutional neural network (CNN) for image classification which was carefully crafted to avoid overfitting. Contrary to most CNNs which apply normalization before pooling, our proposed architecture reverse the order of such tasks. The performance of the proposed architecture, named ACEnet, was evaluated using a hold-out method over five selected databases: Olivia, Paris, Oxford Buildings, Caltech-101, and Caltech-256. We present three main results: processing time, training performance and testing performance for each database. Also, we present a comparison versus the well-known Alexnet architecture, where our CNN proposal improves 5.11% the mean testing performance over the selected databases.
Profile Inference from Heterogeneous Data

One of the essential steps in most business is to understand customers' preferences. In a data-centric era, profile inference is more and more relaying on mining increasingly accumulated and usually anonymous (protected) data. Personalized profile (preferences) of an anonymous user can even be recovered by some data technologies. The aim of the paper is to review some commonly used information retrieval techniques in recommendation systems and introduce new trends in heterogeneous information network based and knowledge graph based approaches. Then business developers can get some insights on what kind of data to collect as well as how to store and manage them so that better decisions can be made after analyzing the data and extracting the needed information.
Top–Down Saliency Detection Based on Deep-Learned Features

How to localize objects in images accurately and efficiently is a challenging problem in computer vision. In this paper, a novel top–down fine-grained salient object detection method based on deep-learned features is proposed, which can detect the same object in input image as the query image. The query image and its three subsample images are used as top–down cues to guide saliency detection. We ameliorate convolutional neural network (CNN) using the fast VGG network (VGG-f) pre-trained on ImageNet and re-trained on the Pascal VOC 2012 dataset. Experiment on the FiFA dataset demonstrates that proposed method can localize the saliency region and find the specific object (e.g., human face) as the query. Experiments on the David1 and Face1 sequences conclusively prove that the proposed algorithm is able to effectively deal with many challenging factors including illumination change, shape deformation, scale change and partial occlusion.
WhatsApp for Defamiliarizing Foundation Year English Learners: A Collaborative Action Research Evaluation

We undertook two cycles within a collaborative action research by asking how using WhatsApp supports second language learners understanding of instruction, promoting, task achievement, enjoyment and creativity of learning English in class and online in Blackboard. We gathered data using a survey which indicated the advantages, challenges and learning opportunities of using WhatsApp in the first cycle. In the second cycle, the focus group, data from Blackboard and observations indicate using WhatsApp was ‘enjoyable’ and ‘refreshing’. The collaborative action research prompted our conclusion for using provided a positive evaluation for using WhatsApp to encourage learning across the university for providing language learning instructions either in class or online in Blackboard.
A heuristic approach on metadata recommendation for search engine optimization

Summary This study aims to recommend metadata for building a high ranking in Search Engine Result Page (SERP) by considering Search Engine Optimizations (SEO). For online marketing, it is important to place their websites on the top rank in a result of search engines. However, on‐page techniques of traditional SEO do not have logical foundation to select metadata. Metadata is an important element to prioritize of websites when search engine indexing for user queries. Thereby, for online marketing, this study proposes a method for recommending metadata, which consists of two steps: i) combining keywords and metadata from high‐ranked websites, and ii) evaluating the importance of terms based on semantic relevance. First, terms are selected with influential keywords and metadata by using their frequency and weight. Second, prioritize the terms according to semantic relevance based on a competitive learning model. We evaluated the validity of the proposed method by using three queries in Google. Experimental results demonstrate that it increases traffic of a website, by using terms, which are high‐ranked websites and semantic relevance.
Shape Clustering Using K-Medoids in Architectural Form Finding

As the number of design candidates in generative systems is often high, there is a need for an articulation mechanism that assists designers in exploring the generated design set. This research aims to condense the solution set yet enhance heterogeneity in generative design systems. Specifically, this work accomplishes the following: (1) introduces a new design articulation approach, a Shape Clustering using K-Medoids (SC-KM) method that is capable of grouping a dataset of shapes with similitude in one cluster and retrieving a representative for each cluster, and (2) incorporate the developed clustering method in architectural form finding. The articulated (condensed) set of shapes can be presented to designers to assist in their decision making. The research methods include formulating an algorithmic set with the implementation of K-Medoids and other algorithms. The results, visualized and discussed in the paper, show accurate clustering in comparison with the expected reference clustering sets.
Overview of Side Channel Cipher Analysis Based on Deep Learning

Abstract According to the widespread use of embedded cryptographic devices, one of the major security threats, side channel attack and protection research, has grown rapidly in the last decade. Along with the popularity of machine learning and deep learning, artificial intelligence technology combined with side channel technology has gradually emerged, which has achieved quite good processing results. Due to the popularity of deep learning in recent years, it is indeed to complete the work with making an overview of side channel analysis based on deep learning. So in order to perfect the theory, this paper begins with the basic theory of the side channel attack. Combined with common models, the methodological analysis of deep learning side channel analysis is summarized and the existing research results are introduced next. Finally, the paper looks forward to the future deep learning side channel technology.
Texture spectral similarity criteria

New similarity criteria capable of assessing spectral modelling plausibility of colour, bidirectional texture functions (BTF), and hyperspectral textures are presented. The criteria credibly compare the multi-spectral pixel values of the textures. They simultaneously consider the pixels of similar values and their mutual ratios. It allows support of the optimal modelling or acquisition setup development by comparing the original data with its synthetic simulations. Analytical applications of the criteria can be spectral based texture retrieval or classification. The suggested criteria together with existing alternatives are extensively tested and compared on a wide variety of colour, BTF, and hyperspectral textures. The performance quality of the criteria is examined in a long series of thousands specially designed monotonically degrading experiments, where proposed ones outperform all tested alternatives.
Automatic Search-and-Replace From Examples With Coevolutionary Genetic Programming

We describe the design and implementation of a system for executing search-and-replace text processing tasks automatically, based only on examples of the desired behavior. The examples consist of pairs describing the original string and the desired modified string. Their construction, thus, does not require any specific technical skill. The system constructs a solution to the specified task that can be used unchanged on popular existing software for text processing. The solution consists of a search pattern coupled with a replacement expression: the former is a regular expression which describes both the strings to be replaced and their portions to be reused in the latter, which describes how to build the modified strings. Our proposed system is internally based on genetic programming and implements a form of cooperative coevolution in which two separate populations are evolved independently, one for search patterns and the other for replacement expressions. We assess our proposal on six tasks of realistic complexity obtaining very good results, both in terms of absolute quality of the solutions and with respect to the challenging baselines considered.
Analysis of sub-pixel laser spot detection in laser triangulation systems

Laser spot detection is an important step of laser triangulation and limits its accuracy. Common methods to determine the center position include circle fitting, the Hough transformation, the gray centroid method or Gaussian fitting. As these algorithms were often tested in different set-ups and under various conditions, they generally lead to diverse results. The aim of this contribution is to investigate the algorithms in a more objective and realistic approach. After a short introduction to laser triangulation, basic information about laser spot center detection is given. Fundamental limitations of the laser spot detection are then considered and analyzed. The investigations are followed by evaluations of measurements in a real laser triangulation setup. Through this approach we could show that the influence of the spatial quantization and the quantization due to the limited bit depth of the detector can be in a similar range as the deviation due to speckle noise in the image. By using adapted image processing steps, the performance of the laser spot center determination could be improved significantly.
An interpretation of radial basis function networks as zero-mean Gaussian process emulators in cluster space

Emulators provide approximations to computationally expensive functions and are widely used in diverse domains, despite the ever increasing speed of computational devices. In this paper we establish a connection between two independently developed emulation methods: radial basis function networks and Gaussian process emulation. The methodological relationship is established by starting from the observation that the concept of correlation between random variables in Gaussian process emulation can be interpreted as a correlation function applied to points in input space. This correlation function is then extended to apply to clusters, i.e. to sets of points. It is then shown that the extended Gaussian process emulation method is equivalent to radial basis function networks, provided that the prior mean in Gaussian process emulation is chosen zero. This elegant connection might increase understanding of the principles of both types of emulation, and might act as a catalyst for mutually beneficial research in emulation domains that were hitherto considered independent.
Applying Support Vector Machine to Electronic Health Records for Cancer Classification

An electronic health record (EHR) is a digital record of patients' clinic information and medical records. The EHRs have been receiving more attentions, due to the extensive amount of valuable medical data, which is able to help medical providers and researchers diagnose and analyze the diseases and healthcare. Cancer diagnosis is very critical to potential patients since finding and treating cancers at an early stage can improve the survival rates. In this study, we use the EHRs obtained in clinics in New York City to classify the cancers. We train the support vector machine (SVM) model with medical records extracted from the EHRs for cancer classification. The model trained with 100 pieces of medical records per cancer, can achieve the prediction accuracy as 86.2% for 10 different types of cancers, and 97.33% for 3 types of cancers if trained with 400 pieces of medical records per cancer.
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings

The task of classifying multi-relational data spans a wide range of domains such as document classification in citation networks, classification of emails, and protein labeling in proteins interaction graphs. Current state-of-the-art classification models rely on learning per-entity latent representations by mining the whole structure of the relations' graph, however, they still face two major problems. Firstly, it is very challenging to generate expressive latent representations in sparse multi-relational settings with implicit feedback relations as there is very little information per-entity. Secondly, for entities with structured properties such as titles and abstracts (text) in documents, models have to be modified ad-hoc. In this paper, we aim to overcome these two main drawbacks by proposing a flexible nonlinear latent embedding model (BRNLE) for the classification of multi-relational data. The proposed model can be applied to entities with structured properties such as text by utilizing the numerical vector representations of those properties. To address the sparsity problem of implicit feedback relations, the model is optimized via a sparsely-regularized multi-relational pair-wise Bayesian personalized ranking loss (BPR). Experiments on four different real-world datasets show that the proposed model significantly outperforms state-of-the-art models for multi-relational classification.