Reviewerly/exHarmony huggingface dataset:
Hand Puppet as Means for eTextile Synthesis

To situate the skills of the textile designer within the HCI-process, we present a case of a hand puppet with a purpose-woven smart textile pattern. The qualities found in traditional textile design are tacitly synthesized into the eTextile-design process. We see this mentality as having a natural dialogue with HCI-practice. The hand puppet consists of two layers: an inner sensor glove, designed to detect the movements of the user's fingers, and a woven outer layer that has a touch sensitive user interface integrated into its woven structure. The two interfaces can be operated simultaneously by two separate users; an adult and a child. Our interest is to understand better how the traditional textile design variables can be utilized in the user interface and -experience design. We aim towards the synthesis of woven eTextile design, consisting of user interface design, pattern design, sensor structure design and textile layout design.
Bee for mining (B4M) – A novel rule discovery method using the Bees algorithm with quality-weight and coverage-weight

This paper proposes a novel tool known as Bee for Mining (B4M) for classification tasks, which enables the Bees Algorithm (BA) to discover rules automatically. In the proposed B4M, two parameters namely quality-weight and coverage-weight have been added to the BA to avoid any ambiguous situations during the prediction phase. The contributions of the proposed B4M algorithm are two-fold: the first novel contribution is in the field of swarm intelligence, using a new version of BA for automatic rule discovery, and the second novel contribution is the formulation of a weight metric based on quailty and coverage of the rules discovered from the dataset to carry out Meta-Pruning and making it suitable for any classification problem in the real world. The proposed algorithm was implemented and tested using five different datasets from University of California, at Irvine (UCI Machine Learning Repository) and was compared with other well-known classification algorithms. The results obtained using the proposed B4M show that it was capable of achieving better classification accuracy and at the same time reduce the number of rules in four out of five UCI datasets. Furthermore, the results show that it was not only effective and more robust, but also more efficient, making it at least as good as other methods such as C5.0, C4.5, Jrip and other evolutionary algorithms, and in some cases even better.
Usage Comparison Between English-Language and French-Language Online Databases in a Bilingual Canadian University

This article reports the results of a quantitative study comparing usage rates between English-language and French-language online databases at the J.N. Desmarais Library of Laurentian University, Canada. Because of discussions surrounding cost and use of French-language electronic resources it became important to determine if French-language subscriptions and purchases are being used to the same level as their English-language counterparts. Although, on the surface, each database collection shared similarities in use, the usage rates do differ between English-language and French-language online database collections, with French-language databases demonstrating consistently lower linear relationships between the size of the collection and its use. However, a cost-per-use analysis revealed next to no difference between the two, with the English-language database collection having a cost-per-search of $0.35 and the French-language collection one of $0.31.
Global Artificial Bee Colony Search Algorithm for Data Clustering

Data clustering is a widespread data compression, vector quantization, data analysis, and data mining technique. In this work, a modified form of ABC, i.e. global artificial bee colony search algorithm (GABCS) is applied to data clustering. In GABCS the modification is due to the fact that experienced bees can use past information of quantity of food and position to adjust their movements in a search space. Due to this fact, solution search equations of the canonical ABC are modified in GABCS and applied to three famous real datasets in this work i.e. iris, thyroid, wine, accessed from the UCI database for the purpose of data clustering and results were compared with few other stated algorithms such as K-NM-PSO, TS, ACO, GA, SA and ABC. The results show that while calculating intra-clustering distances and computation time on all three real datasets, the proposed GABCS algorithm gives far better performance than other algorithms whereas calculating computation numbers it performs adequately as compared to typical ABC.
Upper bounds on the quantum capacity for a general attenuator and amplifier

There have been several upper bounds on the quantum capacity of the single-mode Gaussian channels with thermal noise, such as thermal attenuator and amplifier. We consider a class of attenuator and amplifier with more general noises, including squeezing or even non-Gaussian one. We derive new upper bounds on the energy-constrained quantum capacity of those channels by using the quantum conditional entropy power inequality. Also, we obtain lower bounds for the same channels by means of Gaussian optimizer with fixed input entropy. They give narrow bounds when the transmissivity is near unity and the energy of input state is low.
On How Users Edit Computer-Generated Visual Stories

A significant body of research in Artificial Intelligence (AI) has focused on generating stories automatically, either based on prior story plots or input images. However, literature has little to say about how users would receive and use these stories. Given the quality of stories generated by modern AI algorithms, users will nearly inevitably have to edit these stories before putting them to real use. In this paper, we present the first analysis of how human users edit machine-generated stories. We obtained 962 short stories generated by one of the state-of-the-art visual storytelling models. For each story, we recruited five crowd workers from Amazon Mechanical Turk to edit it. Our analysis of these edits shows that, on average, users (i) slightly shortened machine-generated stories, (ii) increased lexical diversity in these stories, and (iii) often replaced nouns and their determiners/articles with pronouns. Our study provides a better understanding on how users receive and edit machine-generated stories, informing future researchers to create more usable and helpful story generation systems.
Exploring Classification of Histological Disease Biomarkers From Renal Biopsy Images

Identification of diseased kidney glomeruli and fibrotic regions remains subjective and time-consuming due to complete dependence on an expert kidney pathologist. In an attempt to automate the classification of glomeruli into normal and abnormal morphology and classification of fibrosis patches into mild, moderate and severe categories, we investigate three deep learning techniques: traditional transfer learning, pre-trained deep neural networks for feature extraction followed by supervised classification, and a novel Multi-Gaze Attention Network (MGANet) that uses multi-headed self-attention through parallel residual skip connections in a CNN architecture. Emperically, while the transfer learning models such as ResNet50, InceptionResNetV2, VGG19 and InceptionV3 acutely under-perform in the classification tasks, the Logistic Regression model augmented with features extracted from the InceptionResNetV2 shows promising results. Additionally, the experiments effectively ascertain that the proposed MGANet architecture outperforms both the former baseline techniques to establish the state of the art accuracy of 87.25% and 81.47% for glomerluli and fibrosis classification, respectively on the Renal Glomeruli Fibrosis Histopathological (RGFH) database.
A Layered Reference Architecture for Metamodels to Tailor Quality Modeling and Analysis

Nearly all facets of our everyday life strongly depend on software-intensive systems. Besides correctness, highly relevant quality properties of these systems include performance, as directly perceived by the user, and maintainability, as an important decision factor for evolution. These quality properties strongly depend on architectural design decisions. Hence, to ensure high quality, research and practice is interested in approaches to analyze the system architecture for quality properties. Therefore, models of the system architecture are created and used for analysis. Many different languages (often defined by metamodels) exist to model the systems and reason on their quality. Such languages are mostly specific to quality properties, tools or development paradigms. Unfortunately, the creation of a specific model for any quality property of interest and any different tool used is simply infeasible. Current metamodels for quality modeling and analysis are often not designed to be extensible and reusable. Experience from generalizing and extending metamodels result in hard to evolve and overly complex metamodels. A systematic way of creating, extending and reusing metamodels for quality modeling and analysis, or parts of them, does not exist yet. When comparing metamodels for different quality properties, however, substantial parts show quite similar language features. This leads to our approach to define the first reference architecture for metamodels for quality modeling and analysis. A reference architecture in software engineering provides a general architecture for a given application domain. In this paper, we investigate the applicability of modularization concepts from object-oriented design and the idea of a reference architecture to metamodels for quality modeling and analysis to systematically create, extend and reuse metamodel parts. Thus, the reference architecture allows to tailor metamodels. Requirements on the reference architecture are gathered from a historically grown metamodel. We specify modularization concepts as a foundation of the reference architecture. Detailed application guidelines are described. We argue the reference architecture supports instance compatibility and non-intrusive, independent extension of metamodels. In four case studies, we refactor historically grown metamodels and compare them to the original metamodels. The study results show the reference architecture significantly improves evolvability as well as need-specific use and reuse of metamodels.
Unsupervised Quality Estimation Without Reference Corpus for Subtitle Machine Translation Using Word Embeddings

We demonstrate the potential for using aligned bilingual word embeddings to create an unsupervised method to evaluate machine translations without a need for parallel translation corpus or reference corpus. We explain why movie subtitles differ from other text and share our experimental results conducted on them for four target languages (French, German, Portuguese and Spanish) with English source subtitles. We propose a novel automated evaluation method of calculating edits (insertion, deletion, substitution and shifts) to indicate translation quality and human aided post edit requirements to perfect machine translation.
Additive generators of fuzzy operations in the form of linear fractional functions

This paper is devoted to the analysis of additive generators in the form of linear fractional functions. The limitations on the coefficients of increasing and decreasing generators are defined. It is shown that the compositions of increasing and/or decreasing generators are also generators. For each case, the corresponding triangular norms or conorms are found. It is established that the triangular norms and conorms obtained on the basis of linear fractional functions, and which are also dual to them, have the same structure. In fact, a new family of dual triangular norms and conorms is proposed.
Double Shellf

Advances in 3D capture technology have made it easier to generate a realistic avatar, which can represent a person in virtual environments. Because avatars can be easily duplicated in the virtual environments, there can be an unrealistic situation where a person sees her/his own doppelgangers. Doppelganger is a double of a person and sometimes portrayed as a sinister existence. To investigate how people feel and react when they face their doppelgangers, we developed "Double Shellf", a virtual reality experience in which people can interact with their virtual doppelgangers in various situations. In this paper, we introduce the design of Double Shellf and discuss the reactions of 86 users. The user study revealed that most people felt intense eeriness when they see their doppelgangers which acts autonomously and when they were touched by their doppelgangers. We also found that there is a gender difference in reactions to their doppelgangers. We explore the effective way of utilizing doppelgangers.
Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators

This paper presents a novel approach to train GANs for discrete sequence generation without resorting to an explicit neural network as the discriminator. We show that when an alternative mini-max optimization procedure is performed for the value function where a closed form solution for the discriminator exists in the maximization step, it is equivalent to directly optimizing the Jenson-Shannon divergence (JSD) between the generator’s distribution and the empirical distribution over the training data without sampling from the generator, thus optimizing the JSD becomes computationally tractable to train the generator that generates sequences of discrete data. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over existing methods to train GANs that generate discrete sequences.
Spatiotemporal information deep fusion network with frame attention mechanism for video action recognition

In the deep learning-based video action recognition, the function of the neural network is to acquire spatial information, motion information, and the associated information of the above two kinds of information over an uneven time span. We propose a network for extracting semantic information of video sequences based on the deep fusion feature of local spatial–temporal information. Convolutional neural networks (CNNs) are used in the network to extract local spatial information and local motion information, respectively. The spatial information is in three-dimensional convolution with the motion information of the corresponding time to obtain local spatial–temporal information at a certain moment. The local spatial–temporal information is then input into the long- and short-time memory (LSTM) to obtain the context relationship of the local spatial–temporal information in the long-time dimension. We add the ability of the regional attention mechanism of video frames in the neural network mechanism for obtaining context. That is, the last layer of convolutional layer spatial information and the first layer of the fully connected layer are, respectively, input into different LSTM networks, and the outputs of the two LSTMs at each time are merged again. This enables a fully connected layer that is rich in categorical information to provide a frame attention mechanism for the spatial information layer. Through the experiments on the three action recognition common experimental datasets UCF101, UCF11, and UCFSports, the spatial–temporal information deep fusion network proposed has a high correct recognition rate in the task of action recognition.
Markerless Multiview Motion Capture with 3D Shape Model Adaptation

Abstract In this paper, we address simultaneous markerless motion and shape capture from 3D input meshes of partial views onto a moving subject. We exploit a computer graphics model based on kinematic skinning as template tracking model. This template model consists of vertices, joints and skinning weights learned a priori from registered full‐body scans, representing true human shape and kinematics‐based shape deformations. Two data‐driven priors are used together with a set of constraints and cues for setting up sufficient correspondences. A Gaussian mixture model‐based pose prior of successive joint configurations is learned to soft‐constrain the attainable pose space to plausible human poses. To make the shape adaptation robust to outliers and non‐visible surface regions and to guide the shape adaptation towards realistically appearing human shapes, we use a mesh‐Laplacian‐based shape prior. Both priors are learned/extracted from the training set of the template model learning phase. The output is a model adapted to the captured subject with respect to shape and kinematic skeleton as well as the animation parameters to resemble the observed movements. With example applications, we demonstrate the benefit of such footage. Experimental evaluations on publicly available datasets show the achieved natural appearance and accuracy.
A Structural Evolving Approach for Fuzzy Systems

A structural evolving approach (SEA) based on incremental partitioning learning is proposed in this paper. SEA starts with a simple fuzzy system with one fuzzy rule containing no fuzzy term for the antecedent part. After that, it keeps evolving by adding new fuzzy terms to the selected attribute in the selected partition. A new partitioning technique that locates sufficient splitting points is proposed. Furthermore, a dynamic partition-selection technique that leads to the best tradeoff between accuracy and interpretability is presented. In addition, a rule reduction mechanism that is able to locate rules with low and high impact on the system is developed. Finally, a local linear optimization is used to find the consequent parameters, which means that SEA uses only linear systems to find the antecedents and consequents parameters. Therefore, a simple and high interpretability system is offered. Six data sets are used to validate the performance of SEA with similar works. Despite the low complexity of SEA, the results show that SEA outperforms the existing methods with fewer fuzzy rules and fewer antecedent conditions too.
Self Hyper-parameter Tuning for Stream Recommendation Algorithms

E-commerce platforms explore the interaction between users and digital content – user generated streams of events – to build and maintain dynamic user preference models which are used to make meaningful recommendations. However, the accuracy of these incremental models is critically affected by the choice of hyper-parameters. So far, the incremental recommendation algorithms used to process data streams rely on human expertise for hyper-parameter tuning. In this work we apply our Self Hyper-Parameter Tuning (SPT) algorithm to incremental recommendation algorithms. SPT adapts the Melder-Mead optimisation algorithm to perform hyper-parameter tuning. First, it creates three models with random hyper-parameter values and, then, at dynamic size intervals, assesses and applies the Melder-Mead operators to update their hyper-parameters until the models converge. The main contribution of this work is the adaptation of the SPT method to incremental matrix factorisation recommendation algorithms. The proposed method was evaluated with well-known recommendation data sets. The results show that SPT systematically improves data stream recommendations.
High-throughput and area-efficient fully-pipelined hashing cores using BRAM in FPGA

In this paper, an area-efficient fully-pipelined architecture of SHA-1 and SHA-256 implemented on FPGA is proposed for achieving high operating frequency and throughput. The conventional pipeline architecture consumes a lot of registers, especially the consumption increases dramatically for the higher number of pipeline stage. To solve this problem, a new scheme using block RAM (BRAM) is presented to reduce consumption of registers and make the fully-pipelined architecture simpler. Additionally, to achieve operating frequency greater than 300 MHz, the new sub-cores of SHA-1 and SHA-256 combined with the loop unrolling and pre-computation techniques are introduced to the design. Compared to previous works, the throughput and throughput/Slice of SHA-1 and SHA-256 in proposed designs are substantially increased to 159.590 Gbps, 16.083 Mbps/slice and 154.880 Gbps, 10.94 Mbps/slice respectively on Kintex-7 FPGA.
Mutual equidistant-scattering criterion: A new index for crisp clustering

Clustering algorithms usually assume that the number K of clusters is known, although there is often no prior knowledge about the underlying set. Consequently, the significance of the defined groups needs to be validated. Cluster validity indexes are commonly used to perform the validation of clustering results. However, most of them are considered to be dependent on the number of data objects and often tend to ignore small and low-density groups. Furthermore, suboptimal clustering solutions are frequently selected when the clusters are in a certain degree of overlapping or low separation. Thus, we propose a new non-parametric internal validity index based on within-cluster mutual equidistant-scattering for crisp clustering. Eight different validity indexes were analysed to detect the number of clusters in a data set. Experiments on both synthetic and real-world data show the effectiveness and reliability of our approach to evaluate the hyperparameter K.
A novel GPU-aware Histogram-based algorithm for supporting moving object segmentation in big-data-based IoT application scenarios

Multimedia data are a popular case of Big Data that expose the classical 3V characteristics (i.e., volume, velocity and variety). Such kind of data are likely to be processed within the core layer of Internet of Things (IoT) platforms, where a multiple, typically high, number of “things” (e.g., sensors, devices, actuators, and so forth) collaborate to massively process big data for supporting intelligent algorithms running over them. In such platforms, the computational bottleneck is very often represented by the component running the main algorithm, while communication and cooperation costs still remain relevant. Inspired by this emerging trend of big-data-based IoT applications, in this paper we focus on the specific application context represented by the problem of supporting moving object segmentation over images originated in the context of big multimedia data, and we propose an innovative background maintenance approach to this end. In particular, we provide a novel GPU-aware Histogram-based Moving Object Segmentation algorithm that adopts a pixel-oriented approach and it is based on Graphic Processing Units (GPU), called pixHMOS_gpu. pixHMOS_gpu allows us to achieve higher performance, hence making the computational gap of big-data-based IoT applications decisively smaller. Experimental results clearly confirm our arguments.
Hand Gesture-based Wearable Human-Drone Interface for Intuitive Movement Control

Although Radio Control (RC) has been a dominant device for controlling a drone, it is known that a fair amount of training period is required to master it. One way to sidestep such an RC-based control scheme would be utilizing either Kinect or Leap Motion sensor by which the user interacts with a drone more naturally. In such cases, however, the pilot has to hang around the sensor since the operating distance of such sensors is rather short. In this study, we propose a new wearable human-drone interface embedded on a Raspberry Zero, by which even a novice can let the drone not only take-off, land and fly to the intended directions according his hand-pose gestures but also make diverse flying trajectories such as circle, square and spiral using a sequence of hand gestures. Results from Gazebo simulator and several field experiments combined with a personalized calibration program demonstrate the feasibility of its commercial applications.