Reviewerly/exHarmony huggingface dataset:
Perceptions of Integrating Augmented Reality into Network Cabling Tutors

As networks become increasingly complex, professionals must familiarise themselves with the cabling rack to administer the network effectively. In line with this industry goal, we compared the usability, task load and perceptions of three similar network cabling tutoring systems: (1) a hand-held Augmented Reality (AR)-based cabling tutor (HAR); (2) a head-mounted AR-based cabling tutor (HMD) and (3) a 2-Dimensional (2D)-based cabling tutor (HH). While usability of different modalities have been compared previously, none of those comparisons used knowledge modelling approaches. So, in our comparison, each tutor uses knowledge space modelling (KSM) approaches to detect learner mistakes and show arrows on the rack to indicate the source of the mistake. While adding an AR sub-system to a network cabling tablet-based tutor may not necessarily improve usability, participants reported higher engagement over the AR hand held tablet when using the Head-Mounted Display (HMD) condition. Several potential reasons were identified to account for the side effect such as the potential for the AR sub-system to potentially influence learning perceptions; the additional physical effort of needing to point the tablet at the rack and perceived performance degradation.
Cross-domain Recommendation via Multi-layer Graph Analysis Using User-item Embedding

This paper presents cross-domain recommendation via multi-layer graph analysis using user-item embedding. The proposed method constructs two graphs in source and target domains utilizing user-item embedding, respectively. By training relationship between the user's embedding features in these two graphs, the proposed method realizes the cross-domain recommendation via the multi-layer graphs. This is the main contribution of this paper. Then the proposed method can estimate the user's embedding in the target domain from that in the source domain. Finally, our method can recommend items to users via the estimated user's embedding. Experiments on real-world datasets verify the effectiveness of the proposed method.
A GraphBLAS solution to the SIGMOD 2014 Programming Contest using multi-source BFS

The GraphBLAS standard defines a set of fundamental building blocks for formulating graph algorithms in the language of linear algebra. Since its first release in 2017, the expressivity of the GraphBLAS API and the performance of its implementations (such as SuiteSparse: GraphBLAS) have been studied on a number of textbook graph algorithms such as BFS, single-source shortest path, and connected components. However, less attention was devoted to other aspects of graph processing such as handling typed and attributed graphs (also known as property graphs), and making use of complex graph query techniques (handling paths, aggregation, and filtering). To study these problems in more detail, we have used GraphBLAS to solve the case study of the 2014 SIGMOD Programming Contest, which defines complex graph processing tasks that require a diverse set of operations. Our solution makes heavy use of multi-source BFS algorithms expressed as sparse matrix-matrix multiplications along with other GraphBLAS techniques such as masking and submatrix extraction. While the queries can be formulated in GraphBLAS concisely, our performance evaluation shows mixed results. For some queries and data sets, the performance is competitive with the hand-optimized top solutions submitted to the contest, however, in some cases, it is currently outperformed by orders of magnitude.
Utber: Utilizing Fine-Grained Entity Types to Relation Extraction with Distant Supervision

Recently, much effort has been paid to relation extraction during the construction of large ontological knowledge bases (KBs). However, most of the traditional relation extraction systems rely on human-annotated data for training, which requires expensive human effort. Therefore, Distant supervision is proposed to assist the creation of large amounts of labeled data. By this method, an existing KB is heuristically aligned to texts, and the alignment data are treated as training data. Nevertheless, the noise in the training data may cause two serious problems. First, the heuristic label alignment may fail and cause the wrong label problem. Second, the existing statistical models are applied to ad-hoc features, and hence perform poorly due to the dynamic features of noisy data. To address these two problems, in this paper, we propose a novel framework for automatic relation extraction from unstructured text corpora. Specifically, to solve the first problem, we propose a fine-grained entity typing technique to filter wrong data by choosing positive entity type pairs and conduct joint instance-type selection over bag of instances. To solve the second problem, instead of directly defining manually crafted features, we propose a deep neural architecture with attention mechanism to automatically learn positive and negative instance features. Extensive experiments on real-world datasets demonstrate that our method outperforms the competitive state-of-the-art techniques in terms of effectiveness.
Multi-user High Rate Security Differential Chaos Shift Keying

In order to improve the transmission rate and security of multi-user differential chaos shift keying (DCSK) systems, a multi-user high rate security DCSK (MHS-DCSK) based on short reference is proposed. The proposed scheme uses a scrambling matrix and a shift matrix to construct a set of scrambling matrices. These scrambling matrices not only enhances the security of the multi-user short reference system but also increases the bit rate. The proposed scheme employs the correlation properties of chaotic sequences to cancel the multi-user interference (MUI). The simulation results show the effectiveness of the proposed scheme.
Fast Portrait Segmentation of the Head and Upper Body

Portrait segmentation is the process whereby the head and upper body of a person is separated from the background of an image or video stream. This is difficult to achieve accurately, although good results have been obtained with deep learning methods which cope well with occlusion, pose and illumination changes. These are however, either slow or require a powerful system to operate in real-time. We present a new method of portrait segmentation called FaceSeg which uses fast DBSCAN clustering combined with smart face tracking that can replicate the benefits and accuracy of deep learning methods at a much faster speed. In a direct comparison using a standard testing suite, our method achieved a segmentation speed of 150 fps for a 640x480 video stream with median accuracy and F1 scores of 99.96% and 99.93% respectively on simple backgrounds, with 98.81% and 98.13% on complex backgrounds. The state-of-art deep learning based FastPortrait / Mobile Neural Network method achieved 15 fps with 99.95% accuracy and 99.91% F1 score on simple backgrounds, and 99.01% accuracy and 98.43 F1 score on complex backgrounds. An efficacy-boosted implementation for FaceSeg can achieve 75 fps with 99.23% accuracy and 98.79% F1 score on complex backgrounds.
Reliability Estimating By Demographic Matrix in Item-based Recommender Systems

Nowadays, with the growth of communication between both users and websites, recommender systems have gained significant essential. These systems filter information to find out the user's interests and make personalized recommendations for them. Currently, it is important to provide high-reliability recommendations, because if the recommendations are unreliable, the system may lose the user at the very beginning. In this paper, a Demographic Matrix of users is proposed, then for estimating the reliability of predictions, we combined it with similarity or entropy matrix between items. Finally, we evaluated our approach by comparing it to some other reliability estimation algorithms by MAE (Mean Absolute Error). The slope of a regression line helps to determine how quickly our MAE change by the increase of reliability values, and in this way, we calculated the impact of our method on MAE reduction. The experiments on MovieLens dataset show that the proposed reliability estimation algorithm, due to its massive impact on MAE reduction, is significantly better than other algorithms.
WITHDRAWN: Cognitive psychology for monitoring the amateur athletes training activities and depressive mood

Sports psychologists give athletes powerful tools to help them reach peak performance and personal growth. Over the years, numerous strategies have been developed to help athletes transcend their self-imposed psychological constraints and help create athletic environments that encourage their development. The definition of elite/expert athletes as sport psychology research has shown considerable inconsistency and confusion, which has implications for studies in this field. In this paper, the Heuristic Probabilistic Mathematical model (HPMM) has been proposed to monitor the sports amateur athlete's training activities and determine all the athlete's behaviors in an actual-look training environment with accuracy the correlations between shifts in depressed moods. This paper reviews the issues in consultation with athletes and trainers, develops and modifies interventions, and aims to create an experimental single-case design to evaluate this procedure's usefulness. However, the design has good reliability and validity and proved useful information from behavioral observations and subjective ratings. In the current sports scenario, improvements in the intervention and evaluation process may be required. The experimental results show the effective solution in cognitive psychology to enhance amateur athletes' performance and depressive mood monitoring on cognitive sport psychology.
IoT-NFC Controlled Remote Access Security and an Exploration through Machine Learning

Internet of Things (IoT) is a system that allows to connect the computing devices without the help of human-to-human or human-to-computer interaction. This paper proposes an app-based remote access control door lock security system (RACDLS) along with a short-range wireless communication naming Near Field Communication (NFC). The RACDLS system generates two sides' authentication systems rather than one side authentication like conventional systems. In the conventional system, users which are registered, can enter the premises only. In the proposed RACDLS system, users require permission from the room owner either they are registered or unregistered. Moreover, for maintaining the integrity and the confidentiality of data, a cryptographic technique (e.g.) we consider computational 512 bits hash function. In contrast, apply AES-192 for encrypting the hashed data. In addition, machine learning (ML) shows the performance of the employee activities including prediction with model accuracy. A definitive objective of this paper is to ensure the security of remote access control as well as allow notification of both ends, accessibility, usability, and permissibility of a personnel to enter the premises.
Image Brightness Quantification for HDR

Images' brightness quantification is challenging due to the perceptual nature of brightness. In Standard Dynamic Range (SDR), the most common brightness quantification metrics are the Average Picture Level (APL) and the Frame Average Luminance Level (FALL). These metrics rely on simple image statistics and they adequately quantify SDR images' brightness. However, they fail to sufficiently characterize the High Dynamic Range (HDR) broader luminance range and larger color volume. In this work, we propose a novel HDR image brightness quantification metric that weights each pixel contribution to overall brightness based on their color intensity and location. The proposed method is computationally inexpensive, thus suitable for real time applications. Results show that the proposed method outperforms the state-of-the-art HDR brightness quantification metrics.
Fabriction of Mechanical Segway With Handle

Abstract Segway is a two-wheeled motorized personal vehicle consisting of a platform for the fleet mounted above an axle an upright post surmounted by handle with two guiding wheels, controlled by the rocker switches. Segway is mainly used for the short distance travel where there is no use of fuel and no pollution. The main objective of this project is to produce an easy way to move and a comfortable space for the user and reduce pollution as this model is eco-friendly. The present project proposes a cost effective and innovative design compared to the existing segway available in the market. The proposed project also aims to reduce the e-waste by eliminating few electronic components.
Person Re-Identification in Human Following Scenarios: An Experience with RGB-D Cameras

In order to acquire socially acceptable behaviors and seamlessly interact with humans, robots must employ robust solutions to identify, track and follow the interacting person in common environments. Robots nowadays typically are equipped with a camera which can be used to perform body as well as face recognition. However, as the person moves in the environment, the robot might lose track of the individual, e.g., behind corners or obstacles. We report our experience in the design and implementation of a solution that is able to track and follow a target person and that can re-identify the target if the line of sight is obstructed temporarily during following. Our initial experimentation highlights the impact of the employed sensor and the environment in which the person moves on the accuracy with which a persons can be re-identified in practice.
Investigating Customer-Facing Security Features on South African E-commerce Websites

E-commerce websites often store sensitive customer information and there is the impression that customers are not as concerned about protecting their data as they should be. Instead they often choose convenience over security. There are those who argue that e-vendors do not provide the necessary environment to adequately protect their customers’ data by utilizing multi-factor authentication and by providing customer support that educates and encourages customers to follow security best practices. This study develops criteria to evaluate website security and goes on to investigate how the top 20 South African e-commerce websites perform against this. The results show that multi-factor authentication is underutilized and security in the form of password-based authentication can be improved. Furthermore, despite many customer support channels and resources, there is little emphasis placed on educating and encouraging customers to follow security best practices. The results suggest areas for security improvement in order to build trust in e-commerce websites.
Smart Feeder Robot for Severely Disabled Patients

As Taiwan enters an aging society, the number of disabled people increases accordingly, resulting in a growing burden for longterm care and family care managements. We propose an eye-hand integration system with an infrared sensor attached to the end of the arm. Not only can the accuracy of feeding be controlled, but also the position of the spoon/claw can be micro-controlled while picking up food as cleanly as possible. It is more intelligent than other feeding robot arms. It can automatically adjust the position of the head and face of users of different sizes and orientations. As long as the base of the arm is placed close to the head of the bed, feeding the arm can serve the user without special posture adjustment. At the same time, the users' head are mounted with a laser light and a camera which serves as a lighting sources on multiple dark areas on the food table. Given the lighted instructions, the robot arm is guided to different food plate. Disabled patients can then freely choose the order of eating and enjoy the quality of life. We report the details of constructing such system and presents the image processing algorithms for laser light selection operations.
Resume extraction with conditional random field method

Abstract Information extraction resume is a system used to carry out the automatic information extraction process on resumes of prospective employees to obtain key information including information on names, skills, educational experience, work experience, awards which are the main components in a curriculum vitae of prospective employees. Conditional Random Field (CRF) is one method that can be used to extract information. The step taken in the information extraction process is an annotation documents, tokenization, labeling on each token in the document, feature extraction, and the formation of models that will be used at the classification stage. In this study a system was built that could be used to extract CVs from users in pdf format. This study uses a CV from LinkedIn. Information extraction is done using 15 features and 11 classes. The evaluation of information extraction system for the employee’s resume has a precision value of 85,052%, a recall value of 85,052%, and an f-measure of 78,527%.
A Lie-group based modelling for centroid and shape estimation of a cluster of space debris

In the context of spatial surveillance, we are interested in estimating the outline and centroid position of a cluster of debris from a set of noisy sensor observations. The motion of the pieces of debris is completely driven by Kepler's law, therefore they scatter taking a specific curvature. This spreading resembles that of samples drawn on the Lie group SE(3). For this reason, we propose a reformulation of the cluster observation model on Lie groups to intrinsically capture its shape. Then, we derive an optimization algorithm on Lie group to solve the estimation problem. The presented approach is validated on simulated data and compared to a state-of-the-art method based on a Gaussian process modelling.
Expectation Versus Reality: The Failed Evaluation of a Mixed-Initiative Visualization System

Our research aimed to present the design and evaluation of a mixed-initiative system that aids the user in handling complex datasets and dense visualization systems. We attempted to demonstrate this system with two trials of an online between-groups, two-by-two study, measuring the effects of this mixed-initiative system on user interactions and system usability. However, due to flaws in the interface design and the expectations that we put on users, we were unable to show that the adaptive system had an impact on user interactions or system usability. In this paper, we discuss the unexpected findings that we found from our “failed” experiments and examine how we can learn from our failures to improve further research.
It's (almost) done, I just need to evaluate it!

When conducting empirical research, it can be difficult to find experts to evaluate the obtained results, especially when the work is extensive and requires detailed feedback. In the case of undergraduate and graduate students, having access to Human-Computer Interaction (HCI) experts can be even more challenging. This paper reports a strategy used to overcome this problem by taking advantage of the opportunities that the Brazilian HCI community generates. The strategy, applied to finalize a master's research, consisted of using the IHC 2019 as a space to evaluate the proposed product. The result was the participation of experts with varied profiles, which generated in-depth and detailed feedback - a goal difficult to achieve in the context in which the research was conducted, where HCI researchers are still scarce. In this sense, we hope that sharing our experience can help students who need to evaluate their work but face similar difficulties in finding experts. We also envision this suggestion as a way to increase the integration between students and researchers.
Performance Analysis of MODENAR and MOEA for Mining Association Rules

Evolutionary algorithms have significant impact on association rule mining. These algorithms can be single objective or multi-objective, depending on the intended result. Both Multi-Objective Evolutionary Algorithm (MOEA) and Multi-Objective Differential Evolution Algorithm for Mining Numeric Association Rules (MODENAR) are multi-objective algorithms but uses different objectives. MOEA is an algorithm that mines association rules from a dataset that contains any type of attributes. Contrarily, MODENAR was proposed for mining numeric association rules and this algorithm was not compared to any other algorithms by the authors. To our apprehension, no such work has been done till now that compares MODENAR with other association rule mining algorithms. So, this paper will compare MODENAR with another association rule mining algorithm, named MOEA and demonstrate the results by applying above mentioned algorithms on different datasets.
Live Demonstration: Gaze Following System for Noninvasively Testing Electronic Contact Lens

This demonstration presents a gaze following system for facilitating the experiments with a contact lens. An animatronic eye model that can follow the user's gaze in real-time was developed. The hardware and software design are introduced in this work. This system can assist the development of electronic contact lens by providing an intermedia for noninvasively conducting the experiment before the product is safe for human experiments. The self-developed contact lens can be worn on the eye model instead of an animal/human eye. The eye model can perform the same eye movement as the user in real-time without any physical contact. This allowed to indirectly collect experimental human data and to evaluate the performance of the contact lens, which is helpful to reduce time and cost of contact lens experiment.
BFO-GA Interval Type-2 Fuzzy PD Control applied to a Magnetic Levitation System

This paper presents the optimization of two fuzzy logic control (FLC) schemes applied to a magnetic levitation (MagLev) system. The controller optimization is performed using a hybrid algorithm that combines the techniques of genetic algorithms (GA) and Bacterial Foraging Optimization (BFO) algorithm. Comparisons with a type-1 fuzzy PD (T1-FPD) and an interval type-2 fuzzy PD (IT2-FPD) controllers applied to a MagLev system are addressed via simulations in MATLAB -Simulink. The obtained type-2 controllers are based on the design of type-1 fuzzy logic controllers; from these, the uncertainty is generated to have the type-2 membership functions. The control law is directly calculated from the error position signal of the steel ball, as well as its respective velocity error, which presents interesting control qualities.