Reviewerly/exHarmony huggingface dataset:
Voting is a right: a decade of societal, technological and experiential progress towards the goal of remote-access voting

Purpose This study aims to revisit and extends the work of Rosacker and Rosacker (2012) that called for increased interdisciplinary efforts to address and solve the critical issues (critical success factors) facing technologically-enabled remote-access voting platforms. It builds upon the background platform presented there, which included an historical timeline of information and communication technologies and an e-voting literature review, and extends that work by providing a state-of-the-art update and review of the rapidly changing voter environment from societal, technological and experiential studies over the past decade. Specific focus is directed at technology-enabled, remote-access voting, while also considering the important role technological advances can play in improving voter registration/confirmation procedures. Design/methodology/approach First, a brief review of significant societal and technological changes, including the rapid evolution of the internet of things, is undertaken to frame the discussion. Second, a sample of several technology-enabled, remote-access voting experiments are reviewed and critiqued. Third, currently available technical solutions targeting technology-enabled voter registration and vote casting are offered as the next step in the process that will ultimately lead to remote-access voting becoming widely deployed across smart devices. Finally, some contemporaneous conclusions are tendered. Findings Society and technology-enabled devices have each witnessed myriad changes and advancements in the second decade of the 21st century. These have led to numerous remote-access voting experiments across the globe that have overwhelmingly proven the concept of technology-enabled, remote-access voting to be viable while also identifying/reasserting issues (critical success factors) that continue to restrain its full implementation. Importantly, none of the problems identified is fatal to the concept. Originality/value This study considers the issue of technologically-enabled, remote-access voting focussing on the impacts associated with the portfolio of recent societal and technological advancements including the many vexing concerns and issues presented by the coronavirus pandemic. Social distancing is limiting access to the traditional methods of in-person voting for both election officials and voters bringing into question the November 2020 US national election. Calls for expanded mail voting options and the requisite federal funding required to support these efforts are increasing, widespread and broadly persuasive. Wholly missing in this debate is an exhaustive consideration and discussion of technologically enhanced, remote-access voting systems and their role in filling the void.
Development of multiple object tracking via multifocal attention.

Multifocal attention is the ability to simultaneously attend to multiple objects, and is critical for typical functioning. Although adults are able to use multifocal attention, little is known about the development of this ability. In two experiments, we investigated multifocal attention in 6-8-year-old children and adults using a child-friendly, computerized multiple object tracking task designed to encourage the use of multifocal attention. We also investigated whether multifocal attention in children is deployed independently across left and right hemifields of vision, as in adults. Our results suggest that children's capacity for multifocal attention increases significantly across middle childhood. We also found evidence that at least one signature of hemifield-independent multifocal attention, the bilateral field advantage, can be observed in children. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Analog Continuous-Time Filter Designing for Morlet Wavelet Transform Using Constrained L2-Norm Approximation

In this paper, a novel methodology is proposed to implement the Morlet wavelet transform in an analog circuit. Under the proposed scheme, the impulse response of the linear time-invariant system is used to approximate the Morlet wavelet function. The approximation accuracy is guaranteed by the constrained L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm method, which reduces the approximation error by decreasing free parameters in searching routines. Due to the complex wavelet function, a common-pole strategy is presented for filter construction. With the common poles in real and imaginary parts, partial components can be shared during the analog implementation of the wavelet transform. With the reasonably good approximation for Morlet wavelet function, an analog continuous-time filter is designed based on the Gm-C integrator and orthonormal ladder topology. Application examples are introduced to illustrate the superior performance of the proposed filter. It is shown that the designed analog filter approximates the ideal Morlet wavelet function well in both time and frequency.
CONTINUITY OF EDUCATIONAL PROCESS THROUGH VIRTUAL KINDERGARTEN DURING COVID-19 OUTBREAK – CASE STUDY FROM CROATIA

CONTINUITY OF EDUCATIONAL PROCESS THROUGH VIRTUAL KINDERGARTEN DURING COVID-19 OUTBREAK – CASE STUDY FROM CROATIA
Color for Characters - Effects of Visual Explanations of AI on Trust and Observability

The present study investigates the effects of prototypical visualization approaches aimed at increasing the explainability of machine learning systems in regard to perceived trustworthiness and observability. As the amount of processes automated by artificial intelligence (AI) increases, so does the need to investigate users’ perception. Previous research on explainable AI (XAI) tends to focus on technological optimization. The limited amount of empirical user research leaves key questions unanswered, such as which XAI designs actually improve perceived trustworthiness and observability. We assessed three different visual explanation approaches, consisting of either only a table with classification scores used for classification, or, additionally, one of two different backtraced visual explanations. In a within-subjects design with N = 83 we examined the effects on trust and observability in an online experiment. While observability benefitted from visual explanations, information-rich explanations also led to decreased trust. Explanations can support human-AI interaction, but differentiated effects on trust and observability have to be expected. The suitability of different explanatory approaches for individual AI applications should be further examined to ensure a high level of trust and observability in e.g. automated image processing.
AR Assisted Process Guidance System for Ship Block Fabrication

The hull structure of the ship block is complex and the number of its constituted components is very large. In order to reduce the rework of the construction process, an augmented reality (AR) assisted information visualization and process guidance system is proposed. Based on the 3D model and the fabrication process of the ship block, a fabrication process information (FPI) model of the ship block is established for the augmented reality system. With the help of the Microsoft HoloLens, the human-computer interface is designed by defining gestures and voice commands. The prototype system is developed, and case study and user test are conducted to verify the effectiveness and feasibility of the system.
You Do Not Decide for Me! Evaluating Explainable Group Aggregation Strategies for Tourism

Most recommender systems propose items to individual users. However, in domains such as tourism, people often consume items in groups rather than individually. Different individual preferences in such a group can be difficult to resolve, and often compromises need to be made. Social choice strategies can be used to aggregate the preferences of individuals. We evaluated two explainable modified preference aggregation strategies in a between-subject study (n=200), and compared them with two baseline strategies for groups that are also explainable, in two scenarios: high divergence (group members with different travel preferences) and low divergence (group members with similar travel preferences). Generally, all investigated aggregation strategies performed well in terms of perceived individual and group satisfaction and perceived fairness. The results also indicate that participants were sensitive to a dictator-based strategy, which affected both their individual and group satisfaction negatively (compared to the other strategies).
The Utilization of Naive Bayes and C.45 in Predicting The Timeliness of Students’ Graduation

An assessment of the success of a college is if the student's graduation rate is on time and high every year. The timeliness of students' graduation can be influenced by several factors. This study aims to determine the profile of the students who graduated both on time and not on time given a certain graduation predicate set by the institution and to know the factors influencing students’ gradution. The model used in this study using the NBC to determine the graduation pattern and the Decision tree to determine the influencing factors. In calculating the NBC algorithm using rapidminer, it was found that the profiles of students who graduated on time and late with the predicate of less satisfactory, satisfactory, very satisfactory and cumlaude. In the Desicion Tree calculation, the highest gain values are obtained in the IPK3, IPS1 and IPK2 attributes. This research needs to be developed further by increasing the number of attributes and data, and it is necessary to make a system to determine the accuracy of students’ graduation from the patterns that have been produced so that it can help universities to increase the level of students’ graduation every year.
Fuzzy Metaheuristic Algorithm for Copy - Move Forgery Detection in Image

One of the most common methods for counterfeiting digital images is copy-move forgery detection (CMFD). It implies that part of the image is copied and pasted to another part of the same image. The purpose of such changes is to hide certain image content, or to duplicate image content. The aim of this paper is to propose a new clustering algorithm for edited images. The image is divided into non-overlapping blocks. New fuzzy metric is used to calculate the distance between the blocks. In this research the metaheuristic method of the variable neighbourhood search (VNS) is used for the classification of the block. The aim of the classification is that the division should be on the changed and unchanged blocks. The results of this research are compared with the latest results from the literature dealing with this problem and it is shown that the proposed algorithm gives better results. Publicly available image databases were used. The proposed algorithm was implemented in the Python programming language.
Against Dedicated Methods: Relational Expertise in Participatory Design with People with Dementia

This chapter narrates the search for ways to involve people with dementia in the design process based on insights from three projects, several workshops and an educational module involving people with dementia and their network of caregivers and family. This six year-trajectory was part of a Ph.D. project and started ambitiously with the endeavor to find a dedicated method for involvement, inspired by those developed to involve children or people with aphasia. The aim was to develop a set of design guidelines for (successfully) involving people with dementia in the design process. It became clear that using a set of guidelines as a universal, dedicated, or passe-partout way of working for every person with dementia, would not work. We argue that the foundations for a more suitable individualized approach lie in the build-up of a personal relationship between the person with dementia and the designer. Based on such a personal relationship, ways to facilitate involvement of a person with dementia can be defined and design decisions can be collaboratively taken. Person-Centered Care is seen as a guide in the build-up of the relational expertise that a designer needs in order to collaborate with a person with dementia and that enables designers to value and articulate shared decision making.
Biomedical named entity recognition based on Glove-BLSTM-CRF model

Named entity recognition is a fundamental task of natural language processing. The number of biomedical named entities is huge, the naming rules are not uniform, and the entity word formation is complex, which brings great difficulties to the biomedical named entity recognition. Traditional machine learning algorithms rely heavily on manual extraction of features. The quality of feature extraction directly affects the accuracy of entity recognition. In the biomedical domain, the cost of manually extracting features and annotating data sets is enormous. In recent years, deep learning methods that do not rely on artificial features have made great progress in many domains. This paper proposes a model based on Glove-BLSTM-CRF for biomedical named entity recognition. Firstly, the Glove model is used to train word vector with semantic features, and BLSTM is used to train word vector with character morphological features. The two are combined as the final representation of the word, then input into the BLSTM-CRF deep learning model to recognize the entity categories. The experimental results show that the model has achieved a better result in the JNLPBA 2004 biomedical named entity recognition task without relying on any artificial features and rules, and the F1 value reaches 75.62%.
A Ship Draft Line Detection Method Based on Image Processing and Deep Learning

Abstract The traditional ship draft detection method mainly adopts the method of the human eye observation, which has the problems of large precision error and slow detection speed. Aiming at this problem, this paper proposes a ship draft line detection method based on image processing and deep learning. The color image is first transformed into a grayscale image, and the corner detection is carried out by Harris operator. Contour map is obtained using Canny operator for edge detection, and line segment detection is performed using Hough algorithm. The water surface is determined by calculating the lowest position of the corner point. Then the grayscale image is binarized to increase the contrast of the image, the digital area is determined by the corner positions of the highest and lowest points, and finally the convolutional neural network is used to perform character recognition and calculate the draft of the ship. Tested on videos of various scenes, the error of the proposed method is less than 1cm.
CenterNet3D: An Anchor free Object Detector for Autonomous Driving.

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D Network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundary. To solve this issue, we propose an auxiliary corner attention module to enforce the CNN backbone to pay more attention to object boundaries which is effective to obtain more accurate bounding boxes. Besides, our CenterNet3D is Non-Maximum Suppression free which makes it more efficient and simpler. On the KITTI benchmark, our proposed CenterNet3D achieves competitive performance with other one stage anchor-based methods which show the efficacy of our proposed center point representation.
Multi-perspective modeling of computer sales system Based on Unified Modeling Language

The requirements and implementation plan of the system are defined through software modeling before software implementation. With the increase of software complexity, software modeling often determines the success or failure of a project. UML (Unified Modeling Language) is a standardized Modeling Language for object-oriented software. There are 13 graphics specified in UML2.0 that describe the relationship between the system and the environment, between the system and the practitioner, and between the components of the system from different perspectives. This paper will analyze and model the computer sales system from external, interactive, structural and behavioral perspectives. This system mainly realizes the functions of user browsing computer, purchasing computer, user personal center management and salesman order management. Through modeling, the requirements of the system are gradually refined and improved in an iterative way. The model can clearly guide the development of the system.
Feedback Loop and Bias Amplification in Recommender Systems

Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendations are then consumed by the users, their reaction will be logged and added to the system: what is generally known as a feedback loop. In this paper, we propose a method for simulating the users interaction with the recommenders in an offline setting and study the impact of feedback loop on the popularity bias amplification of several recommendation algorithms. We then show how this bias amplification leads to several other problems such as declining the aggregate diversity, shifting the representation of users' taste over time and also homogenization of the users experience. In particular, we show that the impact of feedback loop is generally stronger for the users who belong to the minority group.
Blockchain Analysis Tool For Monitoring Coin Flow

While cryptocurrencies like Bitcoin have the potential to break traditional financial barriers, there are growing concerns about such currencies being used to fund illegal activities. Blockchain keeps the complete history of all transactions ever performed and each node replicates it. The humongous data it contains can be analyzed to gain useful insights about user transactions as well as the blockchain as a whole. In this paper, we propose an approach to parse and visualize the data of Bitcoin blockchain in a graph structure and carry out analysis that includes tracking and tracing, address clustering and entity tagging. We also try to find patterns in the data at a macro level to provide insights about the overall system. Thus, these efforts lead to foundation work for an analysis tool for getting insights on the coin flow of any financial system including cryptocurrencies.
Entropy-Based Facial Movements Recognition Using CPVM

This work presents a novel classifier called cascaded principle vector machine (CPVM) to fulfill the goal of high-accuracy facial expression recognition. First, the facial electromyogram (FEMG) signals are acquired by the two-channel wireless data acquisition device. Second, the adaptable signal processing algorithm is proposed for FEMG signals. In this algorithm, the discrete wavelet transform (DWT) is applied to FEMG signals for decomposing it into its frequency sub-bands. Statistical analysis is applied to these sub-bands to extract three unique features, namely Shannon entropy, Tsallis entropy, and Renyi entropy. Thirdly, the CPVM is implemented to classify these extracted features. The proposed CPVM is a combination of principal component analysis (PCA) and least-square support vector machine (LSSVM). By using PCA, both the goals of the dimensionality reduction of input features vector and the selection of discriminating features can be reached. Then, LSSVM combined with a one-against-one strategy is executed to classify features. The results show that the best 95% classification accuracy was achieved by the proposed CPVM classifier.
Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers.

Detecting anomalous inputs, such as adversarial and out-of-distribution (OOD) inputs, is critical for classifiers deployed in real-world applications, especially deep neural network (DNN) classifiers that are known to be brittle on such inputs. We propose an unsupervised statistical testing framework for detecting such anomalous inputs to a trained DNN classifier based on its internal layer representations. By calculating test statistics at the input and intermediate-layer representations of the DNN, conditioned individually on the predicted class and on the true class of labeled training data, the method characterizes their class-conditional distributions on natural inputs. Given a test input, its extent of non-conformity with respect to the training distribution is captured using p-values of the class-conditional test statistics across the layers, which are then combined using a scoring function designed to score high on anomalous inputs. We focus on adversarial inputs, which are an important class of anomalous inputs, and also demonstrate the effectiveness of our method on general OOD inputs. The proposed framework also provides an alternative class prediction that can be used to correct the DNNs prediction on (detected) adversarial inputs. Experiments on well-known image classification datasets with strong adversarial attacks, including a custom attack method that uses the internal layer representations of the DNN, demonstrate that our method outperforms or performs comparably with five recently-proposed, competing detection methods.
Analysis of the Application of Artificial Intelligence Technology in the Construction of Smart Campus

Smart campus is the development trend of education information construction, especially the widespread application of artificial intelligence technology. The development and promotion of the Internet of Things, mobile learning equipment, wireless network equipment, and smart software have greatly improved the level of smart campus applications. The construction of education ecosystem is the goal, to explore the application field of artificial intelligence in smart campus, and to propose a development strategy from smart campus to “smart” campus construction.
Robust modelling of binary decisions in Laplacian Eigenmaps-based Echo State Networks

This paper aims to present a framework for supervised binary classification of n-Boolean functions through Echo State Networks endowed with Laplacian Eigenmaps for dimensionality reduction. The proposed method is applied both to improve the classification performance when the learnt weights are quantised in view of a digital implementation and as a computational demonstration of the neural reuse theory when parallel outputs are allowed. Our analysis focuses on the effect of various forms of noise (i.e., normal noise, uniform noise and quantisation noise) when all the possible Boolean functions of n input bits are learnt. External disturbances are applied both over the learnt weights and the input features so that we can analyse how resilient the whole architecture is when various forms of parametric noise is injected into the system. Results presented here show that dimensionality reduction allowed by the Laplacian Eigenmaps-based approach improves robustness to these different sources of noise, leading to reduced memory storage requirements while maintaining high classification performance. Our results are compared to those derived from other more common classification techniques in terms of learning performance and computational complexity, also considering a realistic dataset describing a decision making task in a wall-following navigation session with mobile robots.