Reviewerly/exHarmony huggingface dataset:
Investigation into the reliability of facial recognition systems under the simultaneous influences of mood variation and makeup

Facial recognition systems are increasingly popular and prevalent in our everyday lives, especially on mobile cell phones. This paper is an attempt to investigate the effects that makeup and facial expressions have on the reliability of such systems. While these factors have been shown not to be significant by themselves, it has not been clearly demonstrated whether a combination of both these factors can affect the matching accuracy of the same system in a statistically meaningful way. We carried out numerical experiments through two databases: Radboud Faces database and Psychological Image Collection at Stirling (PICS), using a state of the art algorithm, namely dlib. Then, in order to be able to reliably validate the results, we used two more algorithms (Verilook and VGGFace) to give similarity scores. The results showed that, while the effects of makeup and varied mood expressions are not significant by themselves, the joint effect is. An equal error rate (EER) of 4.68% was achieved when identifying faces under the joint influences of full makeup and mood variation, while the EER under the effect of each of these factors separately is less than 1%.
Fixed Pattern Noise Analysis for Feature Descriptors in CMOS APS Images

This paper provides a comparative performance evaluation of local features for images from CMOS APS sensors affected by fixed pattern noise for different combinations of common detectors and descriptors. Although numerous studies report comparisons of local features designed for ordinary visual images, their performance on images with fixed pattern noise is far less assessed. The goal of this work is to develop a tool that allows to evaluate the performance of computer vision algorithms and their implementations subject to deviations of the physical parameters of the CMOS sensor. This tool will facilitate the quantification of the high-level effects produced by circuit random noise, enabling the optimization of the sensor during the design flow with specifications much closer to the application scope. Likewise, this tool will provide the electronic designer with a relationship between high-level algorithm accuracy and maximum fixed pattern noise. Thus the contribution is double: (1) to evaluate the performance of both local float type and more recent binary type detectors and descriptors when combined under a variety of image transformations, and (2) to extract relevant information from circuit-level simulation and to develop a basic noise model to be employed in the design of the feature descriptor evaluation. The utility of this approach is illustrated by the evaluation of the effect of column-wise and pixel-wise fixed pattern noise at the sensor on the performance of different local feature descriptors.
Immersive Virtual Reality for Fire Safety Behavioural Skills Training via Gesture-based Technology

Abstract The fire safety can be one of the biggest problems in metropolitan area. To reduce injuries in a fire, the virtual reality based behavioural skills training (BST) has been used to train people to survive in emergency circumstance. VR-based BST training has been demonstrated to effectively help people of different ages to acquire safe escape skills in a virtual environment. However, most of people have encountered difficulty to train in a right gesture or poor body performance, such as running at unsuitable body position. Gesture-based technology can effectively help people to learn the correct gestures in order to enhance training efficiency. Thus, this paper proposes a theoretical framework of “VR-Gesture BST”, which is expected to improve behavioural skills training more effectively for fire safety training. It adopts motion capture device obtaining user’s body data to evaluate the body performance during the BST training. The outcomes of the study provide guideline and suggestions for VR based design in practice of the behavioural skills training adopted gesture-based technology.
TOWARDS A CYBERSECURITY CULTURE TOOL THROUGH A HOLISTIC, MULTI-DIMENSIONAL ASSESSMENT FRAMEWORK

Cyber-security culture forms one of the best lines of defence against many cyber-attacks making it an ideal and efficient target to address, in order to better secure organisations.It has its foundations on the information security policies and procedures enforced by its management.It is also dictated and formed by the behaviour, perceptions and attitudes of its employees.These will form the basis of our model which aims to quantify these theoretical constructs in order to analyse them and provide feedback.The close examination of existing security assessment tools and frameworks while identifying the most important security components and sections which need to be addressed and evaluated, resulted in our proposition of a state-of-the-art cyber-security culture tool.The presented approach aims to address existing gaps by adopting a holistic perspective that incorporates both organisational and individual aspects of security culture, including behavioural traits that affect and risk the security level of the company.Our approach and tool will be applied to the EPES sector, where the timely assessment of the security culture is considered essential and of high impact.
Extended, Unscented Kalman, and Sigma Point Multiple Distribution Estimation Filters for Nonlinear Discrete State-Space Models

The extended and unscented Kalman multiple distribution estimation filters (EKMDEF/UKMDEF) were recently proposed for nonlinear continuous-discrete state-space models and their superior filtering accuracy was shown in the simulation of satellite reentry. The EKMDEF/UKMDEF is based on the multiple distribution estimation (MDE) for the filtered state probability density function (PDF) and this letter provides its alternate derivation with a comparison of the Gaussian sum filter (GSF). This result sheds light on the relationship between the EKMDEF/UKMDEF and the GSF and a new filter that is more stable than the EKMDEF/UKMDEF can be designed. The performance of the proposed filter is examined for nonlinear discrete state-space models using benchmark simulation problems and is compared with those of the representative filters including particle filters (PFs).
Detecting Cyberbullying “Hotspots” on Twitter: A Predictive Analytics Approach

The ability to discover cyberbullying “hotspots” on social media is vitally important for purposes of preventing victimization. This study attempts to develop a prediction model for identifying cyberbullying “hotspots” by analyzing the manifestation of charged language on Twitter. A total of 140,000 tweets were collected using a Twitter API during September 2019. The study reports that certain charged language in tweets can indicate a high potential for cyberbullying incidents. Cyberbullies tend to share negative emotion, demonstrate anger, and use abusive words to attack victims. The predictor variables related to “biology,” “sexual,” and “swear” can be further used to differentiate cyberbullies from non-cyberbullies. The study contributes to the detection of cyberbullying “hotspots,” by providing an approach to identify a tendency for cyberbullying activity based on computational analysis of charged language. The contribution is significant for mediation agencies—such as school counseling and law enforcement agencies.
PauseBoard: A Force-Feedback Keyboard for Unintrusively Encouraging Regular Typing Breaks

Maintaining positive digital well-being has become essential as we spend more and more time working at desks in offices, interacting with computers and typing for hours at a time. In this paper we present PauseBoard: A computer keyboard designed to unintrusively encourage users to take regular breaks. Through the use of motorised linear potentiometers, the force required to activate each key is increased towards the end of a set work period, until a maximum level of resistance is reached. Preliminary testing shows that 75% of users respond well to this novel gentle encouragement, being reminded to take breaks while still being able to concentrate and finish their current task, especially when the resistance is increased slowly over time.
Message Latency-Based Load Shedding Mechanism in Apache Kafka

Apache Kafka is a distributed message queuing platform that delivers data streams in real time. Through the distributed processing technology, Kafka has the advantage of delivering very large data streams very fast. However, when the data explosion occurs, the message latency largely increases and the system might be interrupted. This paper proposes a load shedding engine of Kafka that solves this message latency problem. The load shedding engine solves the data explosion problem by introducing a simple mechanism that restricts the transmission of some messages when the latency exceeds the given threshold in the Kafka's producer. Experiments with Apache Storm-based real-time applications show that the latency does not continuously increase due to the load shedding function in both single and multiple data streams, and maintains a constant level. This is the first attempt to apply a load shedding technique to Kafka-based real-time stream processing, providing simple and efficient data explosion control.
An incrementally cascaded broad learning framework to facial landmark tracking

Facial landmark tracking often adopts static models to generically fit per frame of video. This is considered inappropriate since such models ignore the informative correlation between previous and current frames. Moreover, most of these methods fail to balance the speed and accuracy for video-based facial landmark tracking simultaneously. In this paper, we propose an efficient online framework for video-based face alignment, named Incrementally Cascaded Broad Learning framework (ICBL). ICBL aims to continuously enhance the prediction capability of tracking model for sequential data. It is capable of learning the spatial appearance on specific-person statistics from continuous facial frames and using such knowledge to incrementally tune a cascade of regressors in parallel. To achieve this goal, we approximate the facial shape space by sampling from a dynamic distribution which is continuously updated by person-specific statistics from the tracked facial frames. This dramatically facilitates cascade regression to incrementally update all cascade-regressors in parallel, thus allowing a fast update of the whole model. Furthermore, we successfully incorporate both the linear and non-linear mappings into our parallel cascade framework and introduce Broad Learning (BL) algorithm as a solution for them simultaneously. Experimental results on the most popular and large-scale benchmark for facial landmark tracking show highly competitive performance of proposed ICBL in comparisons with the state-of-the-arts. The code of our ICBL framework has been available fromhttps://github.com/CaifengLiu/Facial-landmark-tracking-by-ICBL.
Learning Rust: How Experienced Programmers Leverage Resources to Learn a New Programming Language

Experienced programmers are capable of learning new programming languages independently using various available resources, but we lack a comprehensive understanding of which resources they find most valuable in doing so. In this paper, we study how experienced programmers learn Rust, a systems programming language with extensive documentation and example code, an active online community, and descriptive compiler errors. We develop a task that requires learning Rust syntax and comprehending the Rust-specific approach to mutability and ownership. Our results show that users spend 43% of online time viewing example code and that programmers appreciate in-line compiler errors, choosing to refresh, on average, every 30.6 seconds after first discovering this feature. The average time between these refreshes predicted total task time, but individual resource choices did not. Based on our findings we offer design implications for language and IDE developers.
Motor Memory in HCI

There is mounting evidence acknowledging that embodiment is foundational to cognition. In HCI, this understanding has been incorporated in concepts like embodied interaction, bodily play, and natural user-interfaces. However, while embodied cognition suggests a strong connection between motor activity and memory, we find the design of technological systems that target this connection to be largely overlooked. Considering this, we are provided with an opportunity to extend human capabilities through augmenting motor memory. Augmentation of motor memory is now possible with the advent of new and emerging technologies including neuromodulation, electric stimulation, brain-computer interfaces, and adaptive intelligent systems. This workshop aims to explore the possibility of augmenting motor memory using these and other technologies. In doing so, we stand to benefit not only from new technologies and interactions, but also a means to further study cognition.
Leveraging Connected Vehicles to Provide Enhanced Roadway Condition Information

Real-time performance measures are important for agencies to maintain their roadways during the winter season. However, infrastructure can be expensive to deploy and maintain and may be sparse in rural areas, while speed data alone may not provide enough fidelity in borderline conditions. This study looks at high-frequency vehicle data from the in-vehicle bus to detect changes in the vehicle and driver behavior during changing winter road conditions. The data is reported to the cloud via cellular communication and is viewable in real-time using a map-based web dashboard. Three winter weather events are assessed using in-vehicle data collected from the February–March 2018 period. Using a two-sample Kolmogorov-Smirnov test, we found high significance in the reductions of applied brake pressures and rates of braking in winter versus fair weather conditions before vehicle intervention is necessary. The paper concludes that pairwise comparison of driver change in brake pressure may be a valuable data source indicative of deteriorating road conditions before more severe indicators such as traction control, antilock brake, and/or hazard indicators are activated.
Smoothing Out Binary Linear Codes and Worst-Case Sub-exponential Hardness for LPN

Learning parity with noise (LPN) is a notorious (average-case) hard problem that has been well studied in learning theory, coding theory and cryptography since the early 90's. It further inspires the Learning with Errors (LWE) problem [Regev, STOC 2005], which has become one of the central building blocks for post-quantum cryptography and advanced cryptographic primitives. Unlike LWE whose hardness can be reducible from worst-case lattice problems, no corresponding worst-case hardness results were known for LPN until very recently. At Eurocrypt 2019, Brakerski et al. [BLVW19] established the first feasibility result that the worst-case hardness of nearest codeword problem (NCP) (on balanced linear code) at the extremely low noise rate $$\frac{\log ^2 n}{n}$$ implies the quasi-polynomial hardness of LPN at the high noise rate $$1/2-1/\mathsf {poly}(n)$$ . It remained open whether a worst-case to average-case reduction can be established for standard (constant-noise) LPN, ideally with sub-exponential hardness. We start with a simple observation that the hardness of high-noise LPN over large fields is implied by that of the LWE of the same modulus, and is thus reducible from worst-case hardness of lattice problems. We then revisit [BLVW19], which is the main focus of this work. We first expand the underlying binary linear codes (of the NCP) to not only the balanced code considered in [BLVW19] but also to another code (with a minimum dual distance). At the core of our reduction is a new variant of smoothing lemma (for both binary codes) that circumvents the barriers (inherent in the underlying worst-case randomness extraction) and admits tradeoffs for a wider spectrum of parameter choices. In addition to similar worst-case hardness result obtained in [BLVW19], we show that for any constant $$0<c<1$$ the constant-noise LPN problem is ( $$T=2^{\varOmega (n^{1-c})},\epsilon =2^{-\varOmega (n^{\min (c,1-c)})},q=2^{\varOmega (n^{\min (c,1-c)})}$$ )-hard assuming that the NCP at the low-noise rate $$\tau =n^{-c}$$ is ( $$T'={2^{\varOmega (\tau n)}}$$ , $$\epsilon '={2^{-\varOmega (\tau n)}}$$ , $$m={2^{\varOmega (\tau n)}}$$ )-hard in the worst case, where T, $$\epsilon $$ , q and m are time complexity, success rate, sample complexity, and codeword length respectively. Moreover, refuting the worst-case hardness assumption would imply arbitrary polynomial speedups over the current state-of-the-art algorithms for solving the NCP (and LPN), which is a win-win result. Unfortunately, public-key encryptions and collision resistant hash functions need constant-noise LPN with ( $$T={2^{\omega (\sqrt{n})}}$$ , $$\epsilon '={2^{-\omega (\sqrt{n})}}$$ , $$q={2^{\sqrt{n}}}$$ )-hardness (Yu et al. CRYPTO 2016 & ASIACRYPT 2019), which is almost (up to an arbitrary $$\omega (1)$$ factor in the exponent) what is reducible from the worst-case NCP when $$c= 0.5$$ . We leave it as an open problem whether the gap can be closed or there is a separation in place.
The Ethics of Data-Driven Personas

Quantitative methods are becoming more common for persona creation, but it is not clear to which extent online data and opaque machine learning algorithms introduce bias at various steps of data-driven persona creation (DDPC) and if these methods violate user rights. In this conceptual analysis, we use Gillespie's framework of algorithmic ethics to analyze DDPC for ethical considerations. We propose five design questions for evaluating the ethics of DDPC. DDPC should demonstrate the diversity of the user base but represent the actual data, be accompanied by explanations of their creation, and mitigate the possibility of unfair decisions.
Analysis of mineral content in Hotspring Water samples in Tubbe and Aramaba Village, Pantar Tengah District, Alor-NTT

Abstract Clean water standards are tasteless, colorless and odorless. Hot spring water in Tubbe and Aramaba villages is used by the community for drinking, cooking, washing, bathing and other needs. However, the physical parameters of the water has no longer met the standard because it is slightly colored and has distinctive odor of sulfur. Low quality of water affects the health of people who consume the water. The main problem is clearly seen from the yellow teeth of the people in Tubbe and Aramaba villages. Therefore, it is important to analyzed water quality to determine the levels of iron (Fe), phosphate (PO 4 ) 3+ and sulfate (SO 4 ) 2- in hot spring water in Tubbe Village and Aramaba Village, Pantar Tengah District. Iron content testing has been carried out using an atomic absorption spectrophotometer. UV-Vis spectrophotometer has been used to test the concentration of phosphate and sulfate. The measurement results show that the hot spring water samples in Tubbe Village contain 0.04mg / L iron (Fe), 0.09 mg/L phosphate (PO 4 ) 3+ and 11,31mg/L sulfate (SO 4 ) 2 . This value is below the maximum threshold, according to Minister of Health Regulation No. 149 of 2010. The results of the Aramaba Village are 0.04 mg/L; 0.09 mg/L and 11.31 mg/L for iron (Fe), phosphate (PO 4 ) 3+ and sulfate (SO 4 ) 2- . This value is above the threshold of calcium content according to Minister of Health Regulation No. 149 of 2010.
Improved Accuracy of Vehicle Counter for Real-Time Traffic Monitoring System

Abstract This research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video streams. In previous research, vehicle detection accuracy still needs to be optimized, so it needed to be improved. In the vehicle detection method, there are several parts that greatly affect, one of which is the thresholding technique. Different thresholding methods can affect the results of the background and foreground separation. Based on the results of testing the proposed method can improve accuracy by more than 20% compared to the previous method. The thresholding method has a considerable influence on the final result of vehicle object detection. The results of the average accuracy of the three types of time, i.e. morning, daytime, and afternoon reached 96.01%. These results indicate that the vehicle counting accuracy is very satisfying, moreover, the method has also been implemented in a real way and can run smoothly.
Easy Learning: Augmented Reality Based Environmental Studies for Primary Students

Primary education is every child's fundamental right. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), primary schooling offers learning and educational activities typically intended to provide learners with basic abilities in reading and writing. The students find it difficult to identify trees and animals around them due to the lack of exposure to the natural environment. This research study introduces mobile based application (Easy Learning) which embraced augmented reality technology (AR) to motivate and aid learners in studying Environmental Studies in terms of identification of animals and trees. In order to provide sufficient knowledge about trees and animals, this research focuses on safe internet browsing and summarization for trees and animals. Easy learning suggest safe videos for kids and generates knowledge based questions to evaluate themselves as well. The study also evaluates whether the students like the features of the Easy Learning and the rate of knowledge change, through pre and post questionnaires given at the beginning and at the end of the implementation of Easy Learning. The findings proves that the Easy Learning as an interactive AR based learning instrument, for Environmental Studies which improves the learning curve.
A Semantic Similarity Supervised Autoencoder for Zero-Shot Learning

This Letter proposes a autoencoder model supervised by semantic similarity for zero-shot learning. With the help of semantic similarity vectors of seen and unseen classes and the classification branch, our experimental results on two datasets are 7.3% and 4% better than the state-of-the-art on conventional zero-shot learning in terms of the averaged top-1 accuracy.
SCRAM: Simple Checks for Realtime Analysis of Model Training for Non-Expert ML Programmers

Many non-expert Machine Learning users wish to apply powerful deep learning models to their own domains but encounter hurdles in the opaque model tuning process. We introduce SCRAM, a tool which uses heuristics to detect potential error conditions in model output and suggests actionable steps and best practices to help such users tune their models. Inspired by metaphors from software engineering, SCRAM extends high-level deep learning development tools to interpret model metrics during training and produce human-readable error messages. We validate SCRAM through three author-created example scenarios with image and text datasets, and by collecting informal feedback from ML researchers with teaching experience. We finally reflect upon our feedback for the design of future ML debugging tools.
Can Computer-Generated Speech Have an Age?

The present study examines whether computer-generated speech is perceived to have an age, and if so, whether we can manipulate the perceived age of the voice. We conducted an experimental study with 51 participants where each computer-generated voice had different age-related characteristics such as the speed rate of the voice and frequency of the pitch. Participants listened to vehicle reviews presented by computer-generated voices with age-related characteristics we manipulated and then evaluated the age of the voice. Results show that we can change the perceived age of a computer-generated voice by manipulating the age-related characteristics of the voice. This work contributes to communities of HCI researchers interested in voice user interfaces (VUIs), conversational agents, and age stereotypes.