Reviewerly/exHarmony huggingface dataset:
Room for one more? - Introducing Artificial Commensal Companions

Commensality is defined as "a social group that eats together" and eating in a commensality setting has a number of positive effects on humans. In this paper, we discuss how HCI and technology in general can be exploited to replicate the benefits of commensality for people who choose or are forced to eat alone. We discuss research into and the design of Artificial Commensal Companions that can provide social interactions during food consumption. We present the design of a system, consisting of a toy robot, computer vision tracking, and a simple interaction model, that can show non-verbal social behaviors to influence a user's food choice. Finally, we discuss future studies and applications of this system, and provide suggestions for future research into Artificial Commensal Companions.
Information-Theoretic Joint Probabilistic Data Association Filter

This article proposes a novel information-theoretic joint probabilistic data association filter for tracking unknown number of targets.The proposed information-theoretic joint probabilistic data association algorithm is obtained by the minimization of a weighted reverse Kullback-Leibler divergence to approximate the posterior Gaussian mixture probability density function.Theoretical analysis of mean performance and error covariance performance with ideal detection probability is presented to provide insights of the proposed approach.Extensive empirical simulations are undertaken to validate the performance of the proposed multitarget tracking algorithm.
Fuzzy Systems for Computational Linguistics and Natural Language

A natural language (NL) is any of the languages naturally used by humans, i.e. not an artificial such as programming languages. Natural language generation systems convert information from computer databases into normal-sounding human language and natural language understanding systems convert samples of human language into more formal representations that are easier for computer programs to manipulate. Natural language is basically a system for describing perceptions. In this paper we discuss use of fuzzy logic in natural language processing and computation, the general process of NLP as well as some common techniques used, computational linguistic, linguistic approximation.
ARchemist: Aiding Experimental Chemistry Education Using Augmented Reality Technology

The pursuit for blending specialized knowledge with applicable skills is an emerging trend in the higher education. Therefore, proficiency in practical work is considered increasingly pivotal, especially in STEM areas. We introduce ARchemist - laboratory task management system, aiding students in controlling procedures and reflecting on their actions during hands-on exercises in chemistry. The proposed solution employs augmented reality technology to support learning and offers in-situ assistance for laboratory procedures. We present the operational research prototype designed in a user-centered process. The system was evaluated in a preliminary study in a real-case class scenario. ARchemist was assessed as advantageous over traditional guidance tools and contributing to student's confidence and efficiency, whilst being a promising endeavour to facilitate chemistry education.
An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm

Abstract Hoax news in Indonesia spread at an alarming rate. To reduce this, hoax news detection system needs to be created and put into practice. Such a system may use readers’ feedback and Naïve Bayes algorithm, which is used to verify news. Overtime, by using readers’ feedback, database corpus will continue to grow and could improve system performance. The current research aims to reach this. System performance evaluation is carried out under two conditions ‒ with and without sources (URL). The system is able to detect hoax news very well under both conditions. The highest precision, recall and f-measure values when including URL are 0.91, 1, and 0.95 respectively. Meanwhile, the highest value of precision, recall and f-measure without URL are 0.88, 1 and 0.94, respectively.
transTexture Lamp: Understanding Lived Experiences with Deformation Through a Materiality Lens

This paper provides a materiality perspective to understanding lived experiences with a deformable domestic artefact, named transTexture lamp. The lamp is an interactive light with a deformable lampshade surface. We deployed transTexture lamps in the homes of three professional designers for two months with the aim of exploring possible interactions and engagements with the deformable lamp. Our findings show how participants experienced transTexture through pleasurable interactions and how they experienced deformation over time from reflections on these interactions. Analyzing the data through a materiality lens unpacked a creative process of drawing on the deformable lampshade surface, which results in the accumulation of substrates and transformations of deformations. These findings suggest opportunities for future material-centered interaction design research and practices in HCI.
Using CNNs RNNs and Machine Learning Algorithms for Real-time Crime Prediction

Over the recent years crime rates in Sri Lanka have drastically increased. Main priority of police is to prevent crime occurrences in order to enhance public safety. Criminals use advanced technologies, which make the crime investigations cumbersome. Police officers spend lot of time and effort on these investigations. A wide range of researches are being conducted in the areas of Artificial Intelligence (AI) and Neural Networks to automate crime detection and prediction. In this paper, we present machine learning and deep learning based E-police system to enhance public safety and support law enforcement. Main objective of the system is prevention of crimes. E-Police is an application that helps police officers to get informed about the incidents happening around in real-time. In addition, system provides predictions about possible crimes likely to take place in future so that precautions can be taken to prevent those.
SVQ++: Querying for Object Interactions in Video Streams

Deep neural nets enabled sophisticated information extraction out of images, including video frames. Recently, there has been interest in techniques and algorithms to enable interactive declarative query processing of objects appearing on video frames and their associated interactions on the video feed. SVQ++ is a system for declarative querying on real-time video streams involving objects and their interactions. The system utilizes a sequence of inexpensive and less accurate models (filters), called Progressive Filters (PF), to detect the presence of the query specified objects on frames, and a filtering approach, called Interaction Sheave (IS), to effectively prune frames that are not likely to contain interactions. We demonstrate that this system can efficiently identify frames in a streaming video in which an object is interacting with another in a specific way, increasing the frame processing rate dramatically and speed up query processing by at least two orders of magnitude depending on the query.
SoK: Blockchain Solutions for Forensics

As the digitization of information-intensive processes gains momentum in nowadays, the concern is growing about how to deal with the ever-growing problem of cybercrime. To this end, law enforcement officials and security firms use sophisticated digital forensics techniques for analysing and investigating cybercrimes. However, multi-jurisdictional mandates, interoperability issues, the massive amount of evidence gathered (multimedia, text, etc.) and multiple stakeholders involved (law enforcement agencies, security firms, etc.) are just a few among the various challenges that hinder the adoption and implementation of sound digital forensics schemes. Blockchain technology has been recently proposed as a viable solution for developing robust digital forensics mechanisms. In this chapter, we provide an overview and classification of the available blockchain-based digital forensic tools, and we further describe their main features. We also offer a thorough analysis of the various benefits and challenges of the symbiotic relationship between blockchain technology and the current digital forensics approaches, as proposed in the available literature. Based on the findings, we identify various research gaps, and we suggest future research directions that are expected to be of significant value both for academics and practitioners in the field of digital forensics.
Multi-party Threshold Private Set Intersection with Sublinear Communication

In multi-party threshold private set intersection (PSI), n parties each with a private set wish to compute the intersection of their sets if the intersection is sufficiently large. Previously, Ghosh and Simkin (CRYPTO 2019) studied this problem for the two-party case and demonstrated interesting lower and upper bounds on the communication complexity. In this work, we investigate the communication complexity of the multi-party setting \((n\ge 2)\). We consider two functionalities for multi-party threshold PSI. In the first, parties learn the intersection if each of their sets and the intersection differ by at most T. In the second functionality, parties learn the intersection if the union of all their sets and the intersection differ by at most T.
A Comparison of Surface and Motion User-Defined Gestures for Mobile Augmented Reality

Advancements in Augmented Reality (AR) technologies and processing power of mobile devices have created a surge in the number of mobile AR applications. Nevertheless, many AR applications have adopted surface gestures as the default method for interaction with virtual content. In this paper, we investigate two gesture modalities, surface and motion, for operating mobile AR applications. In order to identify optimal gestures for various interactions, we conducted an elicitation study with 21 participants for 12 tasks, which yielded a total of 504 gestures. We classified and illustrated the two sets of gestures, and compared them in terms of goodness, ease of use, and engagement. The elicitation process yielded two separate sets of user-defined gestures; legacy surface gestures, which were familiar and easy to use by the participants, and motion gestures, which had better engagement. From the interaction patterns of this second set of gestures, we propose a new interaction class called TMR (Touch-Move-Release), which defines for mobile AR.
Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data

The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.
Cultural Influences on Perceptions of Emotions Depicted in Emojis

Previous research suggests that people from different cultures weigh cues in the eyes versus mouth differently while interpreting emotions. In Western cultures, where overt emotional display is the norm, people weigh the mouth region more heavily when interpreting facial emotional expression in comparison with people from Eastern cultures. By contrast, in Eastern cultures, where subtle emotion display is the norm, people weigh the eyes region more heavily in comparison with people from Western cultures. Emojis are frequently used paralinguistic cues that convey emotions. Here, we report the results of an online quasiexperimental study in which emotion cues in the eyes and mouth regions of emojis were manipulated to test for differences in the perception of emotions among Westerners and Easterners (N = 427). Consistent with previous research, relative to one another, Westerners' and Easterners' ratings of the emotional valence (i.e., happiness/sadness) of emojis were influenced more heavily by the mouth and eyes, respectively. Thus, the present study adds to the literature suggesting cultural differences in the use of mouth versus eye cues to interpret emotions and supports the notion that these differences extend to paralinguistic cues such as emojis and, consequently, have implications for digital communication.
Structure and Sensitivity in Differential Privacy: Comparing <i>K</i>-Norm Mechanisms

Differential privacy (DP) provides a framework for provable privacy protection against arbitrary adversaries, while allowing the release of summary statistics and synthetic data. We address the problem of releasing a noisy real-valued statistic vector T, a function of sensitive data under DP, via the class of K-norm mechanisms with the goal of minimizing the noise added to achieve privacy. First, we introduce the sensitivity space of T, which extends the concepts of sensitivity polytope and sensitivity hull to the setting of arbitrary statistics T. We then propose a framework consisting of three methods for comparing the K-norm mechanisms: (1) a multivariate extension of stochastic dominance, (2) the entropy of the mechanism, and (3) the conditional variance given a direction, to identify the optimal K-norm mechanism. In all of these criteria, the optimal K-norm mechanism is generated by the convex hull of the sensitivity space. Using our methodology, we extend the objective perturbation and functional mechanisms and apply these tools to logistic and linear regression, allowing for private releases of statistical results. Via simulations and an application to a housing price dataset, we demonstrate that our proposed methodology offers a substantial improvement in utility for the same level of risk.
An analytical protocol for determining the elemental chemistry of Quaternary sediments using a portable X-ray fluorescence spectrometer

The modern geologist has a plethora of portable digital tools at their disposal including, GPS, in-field computers providing access to imagery, maps, and on site real time geochemistry provided by portable X-ray fluorescence spectrometry (pXRF). This paper presents an analytical protocol for the examination of Quaternary glaciogenic sediments derived from different bedrock terrains using a portable spectrometer. The protocol outlines best practices for the collection of geochemical data using pXRF from glacoiogenic sediments with generally continental crustal abundance elemental concentrations at a fraction of the cost of traditional laboratory methods. The analytical protocol considers 1) sample preparation, 2) sample analysis, and 3) data cleaning, examination and presentation. The protocol takes into consideration pXRF fundamentals related to, instrument configuration, and an assessment of measurement parameters, including peak overlaps, dwell time, instrument drift, in-situ versus processed samples, variable grain size and moisture content effects, and comparison to traditional laboratory methods. Protocol development occurred over a number of years and analysis of over 4000 geological samples and ~9000 analysis of Certified and Standard Reference Materials.
DEEPEYE

Video object detection and action recognition typically require deep neural networks (DNNs) with huge number of parameters. It is thereby challenging to develop a DNN video comprehension unit in resource-constrained terminal devices. In this article, we introduce a deeply tensor-compressed video comprehension neural network, called DEEPEYE, for inference on terminal devices. Instead of building a Long Short-Term Memory (LSTM) network directly from high-dimensional raw video data input, we construct an LSTM-based spatio-temporal model from structured, tensorized time-series features for object detection and action recognition. A deep compression is achieved by tensor decomposition and trained quantization of the time-series feature-based LSTM network. We have implemented DEEPEYE on an ARM-core-based IOT board with 31 FPS consuming only 2.4W power. Using the video datasets MOMENTS, UCF11 and HMDB51 as benchmarks, DEEPEYE achieves a 228.1× model compression with only 0.47% mAP reduction; as well as 15 k × parameter reduction with up to 8.01% accuracy improvement over other competing approaches.
A novel unsupervised adversarial domain adaptation network for remotely sensed scene classification

High-resolution remote sensing scene classification is a widely applicable task. Due to the diversity of natural scenes and acquisition methods, in different satellite images, scenes of the same class are often variable in texture, background, illumination and spatial resolution. Thus, it is hard for a remote sensing sense database to contain enough representative images. In this case, the generalization error of traditional supervised classification methods might be too large to generate ideal results. Domain adaptation (DA) has been applied to image classification by reducing feature distribution discrepancy between the source domain (where labels are available) and the target domain (where images need to be classified). In this paper, we propose an unsupervised adversarial domain adaptation method boosted by a domain confusion network (ADA-BDC) which aims at adapting the images from different domains to appear as if drawn from the same domain and improve the transferability of our classifier. For this purpose, the feature extractor in ADA-BDC makes the source and target distributions closer by training a Generative Adversarial Nets (GAN) model. After that, a transferred classifier trained by transferred source domain features is able to acquire a better classification accuracy on the target domain than a non-transferred classifier. In this paper, the experiments are conducted on four remote sensing scene benchmark datasets which are different in spatial scale, resolution, land-cover pattern, etc. Experimental results show that our proposed method is able to improve the transferability across different datasets and improve the classification overall accuracy by 17.18% on average. The comparative experiments demonstrate that the proposed DA network outperforms the compared state-of-the-art domain adaptation methods on remote sensing image scene classification.
Linear Dependent Type Theory for Quantum Programming Languages

Modern quantum programming languages integrate quantum resources and classical control. They must, on the one hand, be linearly typed to reflect the no-cloning property of quantum resources. On the other hand, high-level and practical languages should also support quantum circuits as first-class citizens, as well as families of circuits that are indexed by some classical parameters. Quantum programming languages thus need linear dependent type theory. This paper defines a general semantic structure for such a type theory via certain fibrations of monoidal categories. The categorical model of the quantum circuit description language Proto-Quipper-M in [28] constitutes an example of such a fibration, which means that the language can readily be integrated with dependent types. We then devise both a general linear dependent type system and a dependently typed extension of Proto-Quipper-M, and provide them with operational semantics as well as a prototype implementation.
Understanding Viewport- and World-based Pointing with Everyday Smart Devices in Immersive Augmented Reality

Personal smart devices have demonstrated a variety of efficient techniques for pointing and selecting on physical displays. However, when migrating these input techniques to augmented reality, it is both unclear what the relative performance of different techniques will be given the immersive nature of the environment, and it is unclear how viewport-based versus world-based pointing methods will impact performance. To better understand the impact of device and viewing perspectives on pointing in augmented reality, we present the results of two controlled experiments comparing pointing conditions that leverage various smartphone- and smartwatch-based external display pointing techniques and examine viewport-based versus world-based target acquisition paradigms. Our results demonstrate that viewport-based techniques offer faster selection and that both smartwatch- and smartphone-based pointing techniques represent high-performance options for performing distant target acquisition tasks in augmented reality.
The Effect of Sociocultural Variables on Sarcasm Communication Online

Online social networks (OSN) play an essential role for connecting people and allowing them to communicate online. OSN users share their thoughts, moments, and news with their network. The messages they share online can include sarcastic posts, where the intended meaning expressed by the written text is different from the literal one. This could result in miscommunication. Previous research in psycholinguistics has studied the sociocultural factors the might lead to sarcasm misunderstanding between speakers and listeners. However, there is a lack of such studies in the context of OSN. In this paper we fill this gap by performing a quantitative analysis on the influence of sociocultural variables, including gender, age, country, and English language nativeness, on the effectiveness of sarcastic communication online. We collect examples of sarcastic tweets directly from the authors who posted them. Further, we ask third-party annotators of different sociocultural backgrounds to label these tweets for sarcasm. Our analysis indicates that age, English language nativeness, and country are significantly influential and should be considered in the design of future social analysis tools that either study sarcasm directly, or look at related phenomena where sarcasm may have an influence. We also make observations about the social ecology surrounding sarcastic exchanges on OSNs. We conclude by suggesting ways in which our findings can be included in future work.
A weighted voting ensemble of efficient regularized extreme learning machine

The exact evaluation of Extreme Learning Machine (ELM) compactness is difficult due to the randomness in hidden layer nodes number, weight and bias values. To overcome this randomness, and other problems such as resultant overfitting and large variance, a selective weighted voting ensemble model based on regularized ELM is investigated. It can strongly enhance the overall performance including accuracy, variance and time consumption. Efficient Prediction Sum of Squares (PRESS) criteria that utilizing Singular Value Decomposition (SVD) is proposed to address the slow execution. Furthermore, an ensemble pruning approach based on the eigenvalues for the input weight matrix is developed. In this work, the ensemble base classifiers weights are calculated based on the same PRESS error metric used for the solutions of the output weight vector (β) in RELM, thus, it can reduce computational cost and space requirement. Different state-of-the-art learning approaches and various well-known facial expressions faces and object recognition benchmark datasets were examined in this work.