Reviewerly/exHarmony huggingface dataset:
Lamps, Curtains, Robots

This project experiments with techniques of smart product collages and schematic scenarios to investigate smart home technologies. Focusing on smart cameras, the artist presents three scenarios and a range of design research artifacts. These scenarios and collages are presented as conceptual tools to help understand and anticipate present and future issues related to digital privacy, security, trust, accountability, and fairness.
Image Recognition Model over Augmented Reality Based on Convolutional Neural Networks Through Color-Space Segmentation

Currently the image recognition and classification implementing Convolutional Neural Networks is highly used, where one of the most important factors is the identification and extraction of characteristics, events, among other aspects; but in many situations this task is left only in charge of the neural network, without establish and apply a previous phase of image processing that facilitates the identification of patterns. This can cause errors at the time of image recognition, which in critical mission scenarios such as medical evaluations can be highly sensitive. The purpose of this paper is to implement a prediction model based on convolutional neural networks for geometric figures classification, applying a previous phase of color-space segmentation as image processing method to the test dataset. For this, it will be carried out the approach, development and testing of a scenario focused on the image acquisition, processing and recognition using an AR-Sandbox and data analysis tools. Finally, the results, conclusions and future works are presented.
Persuasive games in Interactive Spaces

The final goal of persuasive technology is to change people's attitudes, behaviors, or both, through persuasion and social influence, and by using entertainment as the key to encourage people to keep using the persuasive application. In this work, we present the prototype of a persuasive hybrid game played in an Interactive Space, The Hidden Treasure, whose aim is to foster good social behavior in children. The Interactive Space where the game is played and the toolkit used to develop the game are also explained. Finally, the persuasiveness of the game is partly assessed by using a framework used to evaluate this kind of systems.
A new encrypted image watermarking based on DTCWT and random pixel exchange

An encrypted image watermarking algorithm based on random pixel exchange and the alliance of SVD (singular value decomposition) and DTCWT (dual-tree complex wavelet transform) is proposed. Better directionality and shift invariance properties are motivated towards the selection of DTCWT. The invariance of SVD guarantees robustness and stability. The watermark image is preprocessed using random pixel exchange with a logistic key to get the encrypted version. Initially, the original image decomposed with DTCWT and selected the sub band based on the intensity invariance value. Applied the SVD on both selected sub band and encrypted watermark to generate the singular values. Next, the resultant singular values are combined with an appropriate scaling factor. To generate the watermarked image, performed the inverse DTCWT on modified sub bands. In the process of extraction, used the same scaling factor to generate the decrypted watermark image. With the correct logistic key, the watermark image is recovered. Further, transparency analysis and robustness analysis are carried out. High values of PSNR and normalized correlation (NC) indicates the better robustness and transparency of the proposed algorithm.
A Two-Stage Gradient Ascent-Based Superpixel Framework for Adaptive Segmentation

Superpixel segmentation usually over-segments an image into fragments to extract regional features, thus linking up advanced computer vision tasks. In this work, a novel coarse-to-fine gradient ascent framework is proposed for superpixel-based color image adaptive segmentation. In the first stage, a speeded-up Simple Linear Iterative Clustering (sSLIC) method is adopted to generate uniform superpixels efficiently, which assumes that homogeneous regions preserve high consistence during clustering, consequently, much redundant computation for updating can be avoided. Then a simple criterion is introduced to evaluate the uniformity in each superpixel region, once a superpixel region is under-segmented, an adaptive marker-controlled watershed algorithm processes a finer subdivision. Experimental results show that the framework achieves better performance on detail-rich regions than previous superpixel approaches with satisfactory efficiency.
Visual Analysis of Ligand Trajectories in Molecular Dynamics

In many cases, protein reactions with other small molecules (ligands) occur in a deeply buried active site. When studying these types of reactions, it is crucial for biochemists to examine trajectories of ligand motion. These trajectories are predicted with in-silico methods that produce large ensembles of possible trajectories. In this paper, we propose a novel approach to the interactive visual exploration and analysis of large sets of ligand trajectories, enabling the domain experts to understand protein function based on the trajectory properties. The proposed solution is composed of multiple linked 2D and 3D views, enabling the interactive exploration and filtering of trajectories in an informed way. In the workflow, we focus on the practical aspects of the interactive visual analysis specific to ligand trajectories. We adapt the small multiples principle to resolve an overly large number of trajectories into smaller chunks that are easier to analyze. We describe how drill-down techniques can be used to create and store selections of the trajectories with desired properties, enabling the comparison of multiple datasets. In appropriately designed 2D and 3D views, biochemists can either observe individual trajectories or choose to aggregate the information into a functional boxplot or density visualization. Our solution is based on a tight collaboration with the domain experts, aiming to address their needs as much as possible. The usefulness of our novel approach is demonstrated by two case studies, conducted by the collaborating protein engineers.
Calibration of quad-camera measurement systems using a one-dimensional calibration object for three-dimensional point reconstruction

A quad-camera visual measurement system must have a calibration accuracy that is equivalent to its reconstruction accuracy. We investigate the effects of quad-camera calibration methods and propose a method based on a one-dimensional (1-D) object. First, the essential matrices are calculated and combined with the RANSAC method to obtain the image coordinate of the 1-D calibration object. Then, the initial values of the four camera matrices are retrieved from the essential matrices to scale. Subsequently, the Euclidean distance between the calibration points of the 1-D calibration object is used to retrieve the scale factor. Furthermore, an uncertainty analysis of three-dimensional (3-D) point reconstruction is conducted, and based on this, an iterative linear calibration method is introduced to refine the initial calibration results. Finally, a set of calibration objective functions are established by analyzing the measurement process. A synthetic optimizing algorithm is adopted to obtain the high-precision calibration results of the quad-camera measurement system. Experiments are conducted to analyze the calibration accuracy and robustness of the proposed method in the presence of noise. The results indicate that the proposed method can increase the 3-D measurement accuracy and robustness.
Evaluation of Fast Evolutionary Programming, Firefly Algorithm and Mutate-Cuckoo Search Algorithm In Single-Objective Optimization

In this study proposes an evaluation of different computational intelligences, i.e Fast-Evolutionary Algorithm (FEP), Firefly Algorithm (FA) and Mutate-Cuckoo Search Algorithm (MCSA) for solving single-objective optimization problem. FEP and MCSA are based on the conventional Evolutionary Programming (EP) and Cuckoo Search Algorithm (CSA) with modifications and adjustment to boost up their search ability. In this paper, four different benchmark functions were used to compare the optimization performance of these three algorithms. The results showed that MCSA is better compare with FEP and FA in term of fitness value while FEP is fastest algorithm in term of computational time compare with other two algorithms.
INDIRECT: Intent-Driven Requirements-to-Code Traceability

Traceability information is important for software maintenance, change impact analysis, software reusability, and other software engineering tasks. However, manually generating this information is costly. State-of-the-art automation approaches suffer from their imprecision and domain dependence. I propose INDIRECT, an intent-driven approach to automated requirements-to-code traceability. It combines natural language understanding and program analysis to generate intent models for both requirements and source code. Then INDIRECT learns a mapping between the two intent models. I expect that using the two intent models as base for the mapping poses a more precise and general approach. The intent models contain information such as the semantics of the statements, underlying concepts, and relations between them. The generation of the requirements intent model is divided into smaller subtasks by using an iterative natural language understanding. Likewise, the intent model for source code is built iteratively by identifying and understanding semantically related source code chunks.
Online abuse detection: the value of preprocessing and neural attention models

We propose an attention-based neural network approach to detect abusive speech in online social networks. Our approach enables more effective modeling of context and the semantic relationships between words. We also empirically evaluate the value of text pre-processing techniques in addressing the challenge of out-of-vocabulary words in toxic content. Finally, we conduct extensive experiments on the Wikipedia Talk page datasets, showing improved predictive power over the previous state-of-the-art.
The Sub-Additives: A Proof Theory for Probabilistic Choice extending Linear Logic

Probabilistic choice, where each branch of a choice is weighted according to a probability distribution, is an established approach for modelling processes, quantifying uncertainty in the environment and other sources of randomness. This paper uncovers new insight showing probabilistic choice has a purely logical interpretation as an operator in an extension of linear logic. By forbidding projection and injection, we reveal additive operators between the standard with and plus operators of linear logic. We call these operators the sub-additives. The attention of the reader is drawn to two sub-additive operators: the first being sound with respect to probabilistic choice; while the second arises due to the fact that probabilistic choice cannot be self-dual, hence has a de Morgan dual counterpart. The proof theoretic justification for the sub-additives is a cut elimination result, employing a technique called decomposition. The justification from the perspective of modelling probabilistic concurrent processes is that implication is sound with respect to established notions of probabilistic refinement, and is fully compositional.
Use of ICT in decision making of agricultural marketing: Factors determining of farmers’ involvement

The paper investigated the magnitude of social parameters’ impacts on effective use of Information and Communication Technology (ICT) in agricultural marketing by the farmers. In addition, how farmers identify information sources and how they access to those sources in selected areas of Jamalpur district were also examined. Purposive sampling method was used in this investigation and data were collected from eighty farmer’s thorough survey questionnaire. Descriptive statistics, Likert scale and multivariate regression model were used to analyze the data. Multivariate regression model was specified and estimated to identify the factors affecting use of ICT by farmers. The outcome of this study highlights important factors for the use of ICT. It is evident from the findings that the users of ICT are getting better quality information and are hence making significantly better decisions on all aspects of agricultural marketing. Regression analysis revealed that two factors i.e. cultivated land of farmers and level of education were the important factors of using of ICT by farmers. Modern ICT tool such as mobile phone was the most used device by the farmers due to low price and availability. They collected most of the information regarding the marketing activities of their produce by mobile phone from other progressive farmers, traders and agricultural extension workers. Social imperative findings of this paper might be helpful for the policy maker to emphasis on further extension of mobile phone based agricultural marketing information system in Bangladesh.&#x0D; J. Bangladesh Agril. Univ. 17(2): 226–231, June 2019
Lamda-Flow: Automatic Pushdown of Dataflow Operators Close to the Data

Modern data analytics infrastructures are composed of physically disaggregated compute and storage clusters. Thus, dataflow analytics engines, such as Apache Spark or Flink, are left with no choice but to transfer datasets to the compute cluster prior to their actual processing. For large data volumes, this becomes problematic, since it involves massive data transfers that exhaust network bandwidth, that waste compute cluster memory, and that may become a performance barrier. To overcome this problem, we present λFlow: a framework for automatically pushing dataflow operators (e.g., map, flatMap, filter, etc.) down onto the storage layer. The novelty of λFlow is that it manages the pushdown granularity at the operator level, which makes it a unique problem. To wit, it requires addressing several challenges, such as how to encapsulate dataflow operators and execute them on the storage cluster, and how to keep track of dependencies such that operators can be pushed down safely onto the storage layer. Our evaluation reports significant reductions in resource usage for a large variety of IO-bound jobs. For instance, λFlow was able to reduce both network bandwidth and memory requirements by 90% in Spark. Our Flink experiments also prove the extensibility of λFlow to other engines.
Zero-inflated beta distribution applied to word frequency and lexical dispersion in corpus linguistics

Corpus linguistics is the study of language as expressed in a body of texts or documents. The relative frequency of a word within a text and the dispersion of the word across the collection of texts provide information about the word's prominence and diffusion, respectively. In practice, people tend to use a relatively small number of words in a language's inventory of words and thus a large number of words in the lexicon are rarely employed. The zero-inflated beta distribution enables one to model the relative frequency of a word in a text since some texts may not even contain the word under study. In this paper, the expectation of a word's prominence and dispersion are defined under the zero-inflated beta model. Estimates of a word's prominence and dispersion are computed for words in the British National Corpus 1994 (BNC), a 100 million word collection of written and spoken language of a wide range of British English. The relationship between a word's prominence and dispersion is discussed as well as measures that are functions of both prominence and dispersion.
A Dehazing Algorithm Based on Local Adaptive Template for Transmission Estimation and Refinement

Outdoor images are vulnerable to environment and may suffer various distortions.Therefore, preprocessing for images captured in bad weather is particularly important for computer vision system.One of the most common conditions is haze.Image dehazing, especially single image dehazing is a challenging topic since it's an ill-posed problem and needs to rely on extra information or prior.In this paper, we discussed the shortcomings of existing algorithms and proposed a novel step called Local Adaptive Template.The template is used in transmission estimation and transmission refinement.Starting from the target pixel, the template is extracted under the guidance of the similarity function and only contains pixels related to the center point, thus avoids the influence of adjacent objects, even those with blur edges.We then used the template to improve the Dark Channel Prior(DCP) and the Guided Filter(GF) respectively, and effectively avoided the block effect in DCP and the blur in GF.The obtained transmission map is much more accurate, and free from the halo effect.The dehazing result is much clearer and still looks natural without haze residual.Experiments on natural images and synthetic images show that our method achieves better dehazing results than several state-of-art algorithms and can adapt to different situations.
Visual Analytics of Stratigraphic Correlation for Multi-Attribute Well-Logging Data Exploration

Stratigraphic correlation based on well-logging data is able to help geological interpreters analogize and deduce underground sedimentary morphology.A great deal of traditional methods has been studied to automatically or manually achieve stratigraphic correlation, the courses of which are usually time-consuming and not intuitive.Many uncertainties are easily generated to reduce the effectiveness of stratigraphic correlation and the accuracy of geological interpretation.To address this issue, this paper introduces an interactive visual analytics system for identifying correlation patterns and improving correlation accuracies using large-scale well-logging data.First, we propose a novel stratigraphic correlation model with the composition of multi-log curve integration, layer identification, and layer matching.Then, a visualization framework is designed by working closely with domain experts in an iterative manner to get deeper insights into the course of stratigraphic correlation based on a few visual interfaces such as map view, correlation view, matrix view and attribute view.Also, a rich set of interactions is provided allowing interpreters to refine the results of stratigraphic correlation according to domain knowledge and user requirements.Furthermore, case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system for the stratigraphic correlation and geological interpretation.
Exchange unknown quantum states with almost invisible photons

We propose a quasi-counterfactual quantum swap gate for exchanging Alice's unknown photon state and Bob's unknown atomic state under the condition that only Alice's photon may appear in the transmission channel between Alice and Bob, while the probability of the existence of photon in the transmission channel is controllable and can tend to zero. Unlike standard counterfactual quantum communication protocols, quantum states exchange in present scenario is achieved by multiple phase operations, rather than multiple measurements. The total effect of those operations can be considered as a unitary time evolution operator. Therefore, the communication fidelity and efficiency of our protocol are always one if system imperfection and channel noise are not considered. Compared to standard counterfactual communication protocols, our protocol is easy to implement. We also show that it can be easily converted to a standard counterfactual one.
Effect of Artificial Haptic Characteristics on Virtual Reality Performance

In recent years, virtual reality (VR) systems, mainly applied to computer games, have become easily available and widely used by young people. Multipurpose haptic devices for the virtual environment, such as the SPIDAR system, have been developed for everyday training tasks. Therefore, the wide range of use contexts and user requirements should be considered to effectively design and evaluate the systems. However, the measurement of objective quality of VR systems from the user’s experience has not been widely considered, although some questionnaires for evaluating the user’s experience based on the sense of embodiment have been proposed. We investigate the measures relating to the user performance, especially with regard to the sense of embodiment. We find that the duration observed between an event and the root-mean-square electromyogram amplitude dropping to a local minimum, within two seconds, could be a measure for evaluating the quality of VR systems from the point of view of user performance.
Full-field microsurface profilometry using image correlation without vertical scanning

A full-field microprofilometry involving innovative image correlation was developed for profile measurement without vertical scanning. High-speed optical inspection has become critical for confirming precise dimensions in semiconductor fabrication such as microbumping in 3D stacked ICs and precision manufacturing. A digital micromirror device (DMD) is designed to serve as a point-light-source array in a quasiconfocal optical configuration and perform lateral scanning to minimize signal crosstalk between neighboring testing points. More importantly, multiple diffractive images are detected and measured with a prebuilt depth-correlated database to extract the height information of a tested surface. A 100-nm repeatability can be realized in the absence of a detector pinhole and without vertical scanning, thus achieving high-speed submicrometer-scale surface profilometry.
EpidVis: A visual web querying tool for animal epidemiology surveillance

The use of electronic media for the detection and monitoring of animal disease outbreaks is crucial for disease surveillance and early warning systems. Animal health specialists regularly query web pages using various formulations to obtain up-to-date news on disease outbreaks. This task, however, is often manual and time-consuming. Visualization techniques can nevertheless facilitate their web searches, compared to traditional searches. This article presents EpidVis, a visual web query tool designed for experts in animal health, conducting epidemic intelligence activities from news sources on the Internet. It consists of several views that help the domain experts efficiently build and launch queries, as well as visualize the results. Moreover, it supports external information integration to help domain experts enrich their knowledge and adapt their queries. EpidVis was assessed considering usability (user study) and usefulness for experts (case study). The results show that our tool helps domain experts in their daily surveillance tasks, allowing them to extract in timely manner accurate information on disease outbreaks from the web.
Evaluation of the performance of different models for predicting direct normal solar irradiance

Solar energy is considered as a clear and sustainable energy resource, and the application of solar energy includes electric power generation and solar concentrators.The precise estimation of solar irradiance plays an important role in evaluating the performance of active solar energy utilizations such as concentrator photovoltaic systems.While global solar irradiance received by a horizontal surface can be easily measured, the availability of direct normal irradiance (DNI) is quite limited.Many models for predicting DNI have been developed and a number of them provided a satisfactory performance.However, it may be difficult for users to efficiently pick up the appropriate models that can be applied to their projects.This study analyses the solar irradiance data in Hong Kong based on continuous measurements and evaluates the performance of three empirical and machine learning models.The accuracy of individual approaches was evaluated using measured Hong Kong data.The results would be helpful to select suitable DNI prediction models for various applications.