Reviewerly/exHarmony huggingface dataset:
Examining the use of narrative constructs in data videos

Data videos are a highly impactful method of communication and are becoming a prevalent medium for communicating information. While the majority of current research focuses on the cinematic aspects of data videos, very little is known about the narrative methodologies involved. This paper presents our insights derived from an initial exploration of this area. We present a taxonomy based on the analysis of 70 existing data videos examining their narrative and visual approaches. We propose that our taxonomy can be used to explain the characteristics or design of data videos. Applying this taxonomy, we present our observations, including the trend of popular technologies applied in current data videos, the under-utilization of promising methods, and highlight research opportunities in the field.
Conditional GAN based individual and global motion fusion for multiple object tracking in UAV videos

Multiple Object Tracking (MOT) meets great challenges in videos captured by Unmanned Aerial Vehicles (UAVs). Different from traditional videos, due to high altitude and abrupt motion changes of UAVs, the sizes of target objects in UAVs videos are usually very small and the appearance information of target objects is unreliable. The motion analysis is meaningful to associate multiple objects in UAV videos. However, the traditional motion analysis models inevitably suffer from the autonomous motion of UAVs. In this paper, we proposed a Conditional Generative Adversarial Networks (GAN) based model to predict complex motions in UAV videos. We regard the objects motions and the UAV movement as the individual motions and global motions respectively. They are complementary with each other and are employed jointly to facilitate accurate motion prediction. Specifically, a social Long Short Term Memory network is exploited to estimate the individual motion of objects, and a Siamese network is constructed to generate the global motion to reflect the view changes from UAVs, and a conditional GAN is developed to generate the final motion affinity. Extensive experimental results are conducted on public UAV datasets contained various types of objects and 4 different kinds of object detection inputs. Robust motion prediction and improved MOT performance are achieved compared with state-of-the-art methods.
LAC: Library for associative classification

The goal of this paper is to introduce LAC, a new Java Library for Associative Classification. LAC is the first tool that covers the full taxonomy of this classification paradigm through 10 well-known proposals in the field. Furthermore, it includes several measures to quantify the quality of the solutions as well as different input/output data formats. Last but not least, the library also provides a framework to automate experimental studies, supporting both sequential and parallel executions. Thanks to the GPLv3 license, LAC is totally free, open-source and publicly available.
Solving systems of linear algebraic equations via unitary transformations on quantum processor of IBM Quantum Experience

We propose a protocol for solving systems of linear algebraic equations via quantum mechanical methods using the minimal number of qubits. We show that $$(M+1)$$-qubit system is enough to solve a system of M equations for one of the variables leaving other variables unknown, provided that the matrix of a linear system satisfies certain conditions. In this case, the vector of input data (the rhs of a linear system) is encoded into the initial state of the quantum system. This protocol is realized on the 5-qubit superconducting quantum processor of IBM Quantum Experience for particular linear systems of three equations. We also show that the solution of a linear algebraic system can be obtained as the result of a natural evolution of an inhomogeneous spin-1/2 chain in an inhomogeneous external magnetic field with the input data encoded into the initial state of this chain. For instance, using such evolution in a 4-spin chain we solve a system of three equations.
Das Contract - A Visual Domain Specific Language for Modeling Blockchain Smart Contracts

A Blockchain (BC) is a technology that introduces a decentralized, replicated, autonomous, and secure databases. A smart contract (SC) is a transaction embedded in the blockchain that contains executable code and its internal storage, offering immutable execution and record keeping. The SC has enormous potential in automating traditional paper contracts and encoding contract logic into program code. Thus, replacing the role of a notary and a central authority. It may dramatically reduce an effort with administration workload and enforcement of such contracts. In this paper, we propose a new visual domain specific language that can capture the SC in a user-friendly way and eliminate the errors associated with programming since the SC code is automatically generated from models. Finally, an open-source proof-of-concept environment for designing and generating the SC is introduced to demonstrate the feasibility of proposed concepts.
Envisioning the scientific paper of the future

Consider for a moment the rate of advancement in the scientific understanding of DNA. It is formidable; from Fredrich Miescher’s nuclein extraction in the 1860s to Rosalind Franklin’s double helix X-ray in the 1950s to revolutionary next-generation sequencing in the late 2000s. Now consider the scientific paper, the medium used to describe and publish these advances. How is the scientific paper advancing to meet the needs of those who generate and use scientific information? We review four essential qualities for the scientific paper of the future: ( i) a robust source of trustworthy information that remains peer reviewed and is ( ii) communicated to diverse users in diverse ways, ( iii) open access, and ( iv) has a measurable impact beyond Impact Factor. Since its inception, scientific literature has proliferated. We discuss the continuation and expansion of practices already in place including: freely accessible data and analytical code, living research and reviews, changes to peer review to improve representation of under-represented groups, plain language summaries, preprint servers, evidence-informed decision-making, and altmetrics.
Image Super-Resolution Using Capsule Neural Networks

Convolutional neural networks (CNNs) have been widely applied in super-resolution (SR) and other image restoration tasks.Recently, Hinton et al. proposed capsule neural networks to resolve the problem of viewpoint variations in image classification tasks.Each capsule is represented as either a vector or a matrix to encode more object information, such as position, size, direction, etc.Instead of detecting specific features, these capsule neural networks search for the most relevant features using an iterative process.Therefore, capsule neural networks require fewer parameters compared to traditional neural networks.Inspired by these advances, we make use of a capsule neural network to exploit more potential features for image SR.In this paper, we develope two frameworks: the Capsule Image Restoration Neural Network (CIRNN) and the Capsule Attention and Reconstruction Neural Network (CARNN), to incorporate capsules into image SR convolutional neural networks.The CIRNN takes advantage of the rich information encoded in the capsules to reconstruct accurate high-resolution images.The CARNN generates SR attention features by utilizing the robust segmentation capability of the capsules.Our experiments show that both frameworks can enhance SR for most testing datasets.The CIRNN performs better than the CARNN and can achieve better performance than other traditional CNN methods with a similar amount of parameters.
Deep Neural Network: Data Detection Channel for Hard Disk Drives by Learning

The application of a deep neural network (DNN) as the detection channel for hard disk drive (HDD) data recovery at high user bit density and the prominent magnetic transition jitter noise are investigated in this article. Directly trained with the un-equalized readback signals without any prior knowledge of the magnetic recording channel, the DNN can automatically learn the signal characteristics, in particular the correlations between the input signals and the impact from the noise. As a result, the DNN read channel not only adapts the inter-symbol interference (ISI) but also demonstrates strong resilience against the colored magnetic noise. Our simulation results also reveal that to fully harness the learning power of the DNN data detection channel, the neural network inputs must cover the ISI spread. In addition, the training data must be sufficiently representative so that the inductive bias learned by the DNN detection channel can be used as good prior knowledge for actual detection.
Fractal superconducting nanowire avalanche photodetector at 1550 nm with 60% system detection efficiency and 1.05 polarization sensitivity

Superconducting nanowire (nanostrip) single-photon detectors (SNSPDs) have shown unprecedented detection efficiency and timing properties, but simultaneously achieving polarization-insensitive high detection efficiency, low dark-count rate (DCR), fast speed, and low timing jitter remains a challenge. Here we report on our design, fabrication, and characterization of a cascaded superconducting avalanche photodetector composed of 40 nm wide NbTiN nanowires patterned into fractal curves. At the base temperature of 2.7 K, the device exhibits <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>60</mml:mn> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>3</mml:mn> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> system detection efficiency at the wavelength of 1550 nm, 1.05 polarization sensitivity, 220 cps DCR, 4 ns recovery time, and 45 ps timing jitter. This Letter not only demonstrates these combined properties on a single detector that was unobtainable previously, but also shows that that current-crowding effect remarkably permits decent internal detection efficiency. These counter-intuitive results expand the understanding pertaining to the device physics of SNSPDs.
Efficient match pair selection for oblique UAV images based on adaptive vocabulary tree

The primary contribution of this paper is an efficient match pair selection method for oblique unmanned aerial vehicle (UAV) images. First, high overlap degrees and spatial resolutions cause image and feature redundancies in vocabulary tree building and image indexing. To cope with this issue, an image selection strategy and a feature selection strategy are designed to decrease the total number of features. Second, by analysing the distribution of the similarity scores, an adaptive threshold selection method is implemented to determine the number of candidate match pairs for each query image, and it avoids the disadvantages of the fixed number and fixed proportion methods. Then, an algorithm, termed AVT-Expansion, is proposed for the match pair selection and simplification where the initial match pairs are first selected by using the adaptive vocabulary tree (AVT). To simplify the initial match pairs, the AVT method is integrated with our previous MST-Expansion algorithm, which is used to extract a match graph by analysing the image topological connection network. Finally, the proposed method is verified using three UAV datasets captured with different oblique multi-camera systems. Experimental results demonstrate that the efficiency of the vocabulary tree building is improved, with speed-up ratios ranging from 14 to 16, and that high image retrieval precision values are obtained for the three datasets. For match pair selection of oblique UAV images, the proposed method is an efficient solution.
Chatbots for the Information Acquisition at Universities – A Student’s View on the Application Area

Chatbots are currently widely used in many different application areas. Especially for topics relevant at the workplace, e.g., customer support or information acquisition, they represent a new type of natural language-based human-computer interface. Nonetheless, chatbots in university settings have received only limited attention, e.g., providing organizational support about studies or for courses and examinations. This branch of research is just emerging in the scientific community. Therefore, we conducted a questionnaire-based survey among 166 students of various disciplines and educational levels at a German university. By doing so, we wanted to survey (1) the requirements implementing a chatbot as well as (2) relevant topics and corresponding questions that chatbots should address. In addition, our findings indicate that chatbots are suitable for the university context and that many students are willing to use chatbots.
Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics

Recently, in order to satisfy the requirements of commercial communication systems and military communication systems, automatic modulation classification (AMC) schemes have been considered. As a result, various artificial intelligence algorithms such as a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) have been studied to improve the AMC performance. However, since the AMC process should be operated in real time, the computational complexity must be considered low enough. Furthermore, there is a lack of research to consider the complexity of the AMC process using the data-mining method. In this paper, we propose a correlation coefficient-based effective feature selection method that can maintain the classification performance while reducing the computational complexity of the AMC process. The proposed method calculates the correlation coefficients of second, fourth, and sixth-order cumulants with the proposed formula and selects an effective feature according to the calculated values. In the proposed method, the deep learning-based AMC method is used to measure and compare the classification performance. From the simulation results, it is indicated that the AMC performance of the proposed method is superior to the conventional methods even though it uses a small number of features.
Is Compositional Data Analysis (CoDA) a theory able to discover complex dynamics in aqueous geochemical systems?

The study of the structure of compositional changes characterizing a geochemical system in time or space could be the key to understand its dynamics and resilience to environmental and anthropic perturbations. Variables characterizing the composition of an aqueous system (river or ground waters) constitute a multivariate system that moves as a whole due to multiple interrelationships and feed-back mechanisms. If the goal of the research is to understand how the whole moves, the Compositional Data Analysis (CoDA) theory offers the correct statistical framework since aqueous geochemical data pertaining to the simplex sample space. Cases of a compositional matrix can be ranked by using some criterium (e.g. increasing runoff or conductivity values), and the differences between subsequent rows calculated by using the perturbation operator that measures the change in the simplex geometry. The obtained perturbation matrix can be then analyzed by using robust Principal Component Analysis (robust-PCA) to discover the association between variables subjected to proportional perturbations during geochemical processes. The calculus of the robust Mahalanobis distance from the barycenter of this matrix appears to be also useful to reveal presence of intermittency in time or space. Intermittency is highly related to the fractal nature of variability representing an emergent property of self-organized complex dissipative systems since it optimizes the dissipation energy gradients and maximize entropy production. Application examples for the surficial waters of the Alps region and the Arno river basin (Tuscany, Central Italy) reveal that the methodology is powerful and able to discover complex dynamics moving on the boundary between different sample spaces, from the Euclidean one to the fractal one. The proposed approach allows us to shift the perspective to the nature of the dynamic interactions and transitions affecting a geochemical landscape revealing how variability moves and how presence of intermittency and resilient behavior affects evolution and prediction.
Evaluating the effect of gamification on the deployment of digital cultural probes for children

This research proposes a digital Cultural Probe (CP) for children in the context of energy use and awareness. A CP is a user research method that allows participants to self-report by means of tools, artefacts, and tasks that they complete at their own pace. Our CP aims to gather data about the energy related habits and knowledge of the children and their families and also aims to provoke reflection and raise their awareness of energy and environmental issues. It is well known that the main drawback of CPs is their low completion rate and the consequent lack of gathered data. Based on the impact of gamification on users' motivation and engagement, we aim to test the hypothesis that "Gamification may encourage an active and fruitful participation of children in Cultural Probes". CP consists of four tasks designed to gather information about the children and their families' electric consumption and saving habits. We developed the CP as an Android app which was evaluated with children from two primary schools in Barcelona, who interacted with two different app versions (gamified and non-gamified). We designed a comparative experiment that gathered data on CP completion rate, CP task completion rate, and the time required to complete the CP. To our surprise, we found a slight difference in favour of the non-gamified app in all of these measures, though this difference was not statistically significant. However, the difference in the children's awareness of and attitude to energy consumption before and after the realisation of the CP was significant as measured by the Wilcoxon signed-rank test. This was not the case for the effect on energy awareness of the children's relatives. The lessons we learned, which may also prove to be useful for researchers that aim to design cultural probes, are that user research methods for children can be effective enough when designed to be interactive and dynamic (i.e. digital CPs) and that the number of CP tasks may also be a factor to take into account. When the number of tasks is low, the CP is likely to achieve a high completion rate and the additional effort of gamifying the application may be not worthwhile. Then, gamification may support a CP with a large number of tedious tasks. When designers want children to reflect on key issues, the CP should be thoughtfully designed because it is not easy to foster children's reflection and reasoning.
Dominant Color Palette Extraction by K-Means Clustering Algorithm and Reconstruction of Image

In our present age, there are numerous applications and devices for converting a standard picture into a picture, which is built from its predominant shading palettes. 'predominant colors' are the hues in the picture, in which the pixels of the picture contain the particular shading. These hues are then removed and another picture is then framed from this picture. However, this is just a speculation of the issue. In this undertaking, we dive into much more proficient techniques with a specific end goal to remove the prevailing shading palettes from the picture. In this manner, utilizing a grouping calculation and a significantly more ideal instatement technique, effective shading palette is extracted. The grouping calculation being utilized here is K-implies bunching calculations which is much more effective calculation than the other bunching calculations out there. The bunching calculation groups the pixels in bunches in view of their shading. The palettes separated from the picture are then utilized and another picture is reproduced.
Mobile and Adaptive Learning Application for English Language Learning

Mobile learning is considered a new phase of e-learning which gives the opportunity to learn more effectively and efficiently. In addition, the use of mobile devices for leaning is more sophisticated and more useful. The m-learning has become available anywhere and anytime for all students and professors. Moreover, the features of these mobile devices include ease of use in every place and time, very reasonable cost for students, and the ability to communicate through the internet or mobile networks, encouraging the development of many kinds of methods and systems. Now, a huge number of applications, in several domains, are oriented to this kind of mobile device. Researchers have been exploiting this technology to enhance the knowledge of learner (especially foreign languages learning). In this paper, the author proposed an educational system that provides the opportunity for students to learn English language outside the classroom and encourages them to get actively involved in their own learning processes.
Enhancing Long Term Fairness in Recommendations with Variational Autoencoders

Recommender systems have become indispensable for several Web sites, helping users deal with big amounts of data. They are capable of analyzing user/item interactions taking place on-line, and provide each user with a list of suggestions sorted by relevance. Items with the same or very close relevance, however, may occupy different positions in the ranking and may be exposed to completely different levels of attention. This promotes unfair treatment and can only be addressed by a long term strategy.
Flexible structures of lightweight block ciphers PRESENT, SIMON and LED

Security and privacy of the Internet of Things (IoT) systems are critical challenges in many data-sensitive applications. The IoT systems are constrained in terms of execution time, flexibility and computational resources. In recent years, many encryption algorithms have been proposed to provide the security of IoT communication. In this study, flexible and high-throughput hardware structures of the PRESENT, SIMON, and LED lightweight block ciphers are presented for IoT applications. The proposed flexible structures can perform various configurations of these block ciphers to support variable key sizes. For example, the PRESENT, SIMON, and LED ciphers support key sizes (80, 128 bits), (96, 144, 128, 192, and 256 bits), and (64, 128 bits), respectively. Therefore, these architectures provide versatile implementations that enable adaptive security level using a variable key size. In the proposed structures, sub-blocks of the ciphers are implemented based on optimised circuits. In the PRESENT and LED ciphers, the S-boxes are implemented based on area-optimised logic circuits. The implementation results of proposed flexible architectures are achieved in 180 nm CMOS technology. Area, throughput and throughput/area of the proposed structures have improved compared to other related works.