Reviewerly/exHarmony huggingface dataset:
Evaluation of 3D markerless pose estimation accuracy using openpose and depth information from a single RGB-D camera

Safe and efficient Human-Robot Collaboration (HRC) requires recognizing human collaborators intention. Hand pose kinematics early on an ongoing movement can provide information for predicting future human actions. Accurate state-of-the-art methods used for human hand pose estimation are either marker-based or make use of multiple cameras set around the workspace. These approaches introduce inconvenience to the user, necessitate calibration and are bounded to the specific set-up and workspace. On the other hand, using a single RGB-D camera would be less obtrusive for the user and less cumbersome to install. In this work, we use OpenPose to extract 2D keypoints from the RGB raw image and we combine them with the depth information acquired from the RGB-D camera to obtain 3D hand poses. We evaluate the accuracy and discrimination ability of our method in ten different static poses.
A 3D-CNN Siamese Network for Motion Gesture Sign Language Alphabets Recognition

Sign language recognition is developed for assisting hearing- and speech-impaired people who communicate with others by using hand gestures as a nonverbal communication. The system aims to translate still images or video frames of sign gestures to a corresponding message, word or alphabet of a designated language. For the American Sign Language (ASL), there are alphabets of J and Z out of 26 letters which require motion gesture and cannot be recognized using static 2-dimension images as usually possible for the others. Their starting gesture also causes a confusion with the other alphabets, esp. I and D. Samples of these two alphabets J and Z, are scarce. This paper developed a n-shot 3D-CNN deep learning Siamese network model to recognize J and Z and address the said problems. Two experiments were conducted to give insight into the performance of the proposed 3D-CNN Siamese network and the improvement using regularization and dilation techniques. The effect of the number of shot in the Siamese model to the performance of the recognition is also provided in the second experiment.
Decentralized Policy Feedback System for Privacy and Governance using Blockchain and Sentiment Analysis for Smart City Applications

In recent years, Blockchain has gained a lot of attention. From being associated with just Bitcoin and Cryptocurrency to being used by Industry for E-Governance, Monitoring and more. At the heart of it all is cryptographically secure and verifiable Ledger whose copies are maintained across network peers. This paper explores yet another use case of the Blockchain. The main idea is the use of Blockchain for recording Feedback to schemes framed by institutions for their members. It demonstrates how such a system can reduce corruption, increase the privacy of the governed and help frame better policies and regulations by combining Blockchain with Deep Learning by Sentiment Analysis on Edge to analyse the reception of the policy.
How Efficiency and Naturalness Change in Multimodal Interaction in Mobile Navigation Apps

Multimodal interaction has been verified to improve user experience, but the challenge of resource competition and information integration in this human-centered interaction mode may negatively affect the efficiency and naturalness. This paper aims to figure out how efficiency and naturalness change in multimodal interaction in mobile navigation apps, which are mostly used as interactive systems for cars’ navigation tasks. The study was conducted based on the Amap app, with which participants should use the single touch-screen interaction and the gesture-combined-voice interaction, respectively, to complete the same route setting task in the driving environment. With the records of the operation time and participants’ subjective scores of two interaction modes, we analyzed the differences and changes in efficiency and naturalness. The results showed that multimodal interaction was more efficient than single-modality interaction; however, the naturalness of multimodal interaction did not improve significantly.
Understanding How Audio Mediates Experiences of Reminiscence for People Living with Blindness

There has been a growing attention in design and HCI on how technology could be designed to support experiences of reminiscence on past life experiences. Yet, this research has largely overlooked people living with blindness. We present preliminary findings from an ongoing field study with 9 participants living with blindness to understand their experiences of reminiscence. We specifically report on the role of audio recordings as key materials our participants drew on to mediate their experiences of reminiscence. Findings are interpreted to explore opportunities for audio-based interactive technology to better support their practices of capturing, storing and reflecting on the past.
IoT Communicator

The Internet of Things and smart products are becoming ubiquitous in people's everyday lives. However, they rarely provide situational information to facilitate direct interactions. Users often need to check mobile devices to know the system's status. To improve user experience, we developed a communicator component that could be attached to any IoT devices. It consists of a low-cost LED and speaker. We applied Disney's animation principles to design light behaviors and musical soundscapes to convey meaningful messages. The result of our user study showed that when only the lights were provided, most users could explain the system's intention correctly. With the acoustic accompaniments, participants could better perceive the presence of devices. In this adjunct paper, we summarize our research and describe the demonstration settings for the conference. The details of the study could be found in the full paper.
Mapping the use of expert system as a form of cloud-based digital forensics development

Abstract This paper discusses mapping of the use of expert systems for the development of cloud-based digital forensics. Mapping is done to answer the challenges in the development of the amount of data and the types of evidence currently that can affect the process of investigating forensics. The mapping is according to several categories, namely, tools, methods, ontology, and framework. The result is an expert system capable of providing automation to the investigation process, accelerating system performance, becoming a guideline for experts, as a means for investigators, and to get a system that can adapt to the framework.
From immersion to collaborative embodiment: extending collective interactivity in built environments through human-scale audiovisual immersive technologies

This paper discusses the use of human-scale immersive virtual environments as a platform for developing technologies for embodied digital interaction. We propose a three-body framework of spatial typologies in the realm of collaborative room-centered immersive systems that governs the relationship between the immersants, the hardware system of the environment, and the presented virtual environment. Through the lens of this framework, we present several use cases in the Collaborative-Research Augmented Immersive Virtual Environment Laboratory (CRAIVE-Lab) at Rensselaer Polytechnic Institute. Through the narratives of these use cases, it is argued that the proposed framework not only affords the design of interaction systems in room-centered virtual environments with spatial awareness, but also engenders new approaches to envision system design and its integration to the built environment. This framework intends to emphasize the significance of spatial thinking in expanding the creative potentials of body-driven interactive immersive systems.
E3X: Encrypt-Everything-Everywhere ISA eXtensions for Private Computation

The rapid increase of recent privacy attacks has significantly decreased trust on behalf of the users. A root cause to these problems is that modern computer architectures have always been designed for performance, while security protections are traditionally addressed reactively. Practical security protections, such as Intel SGX, rely on processing unencrypted data in the architectural state, which leaves them exposed to software attacks (e.g., SGXpectre). This work revisits the traditional computation stack and introduces a novel computation paradigm, where data is never decrypted in the architectural state. Through our architecture, data are protected with symmetric or asymmetric encryption and the programmer manipulates them directly in the encrypted domain. To increase performance, we exploit data locality by introducing decryption caches in the microarchitectural state. Our proposal addresses all abstraction levels in the computation stack: from microarchitecture to library support for high-level programming. The proposed architecture is instantiated through new assembly instructions, registers and functional units operating on large integers. In our evaluation, we extend the OpenRISC 1000 architecture and develop open-source libraries for C++. As a case study, we employ data-oblivious benchmarks and observe that for benchmarks with high temporal locality, our architecture can achieve comparable performance to processing unencrypted data.
Investigating Opaque Infrastructures

This demo presents the Desktop Odometer, a device that shows users the distance they travel when browsing the web, by tracking the total miles between their current location and the server from where they are requesting information. Upon connecting the Desktop Odometer to their computer via a web browser extension, users are able to see how far information travels while surfing the web in real-time. This new ability to 'see' how far information has to physically travel gives users an opportunity to grapple with and reflect on the physicality of the Internet's infrastructure, which otherwise may remain opaque.
Convenient antiderivatives for differential linear categories

Abstract Differential categories axiomatize the basics of differentiation and provide categorical models of differential linear logic. A differential category is said to have antiderivatives if a natural transformation , which all differential categories have, is a natural isomorphism. Differential categories with antiderivatives come equipped with a canonical integration operator such that generalizations of the Fundamental Theorems of Calculus hold. In this paper, we show that Blute, Ehrhard, and Tasson's differential category of convenient vector spaces has antiderivatives. To help prove this result, we show that a differential linear category – which is a differential category with a monoidal coalgebra modality – has antiderivatives if and only if one can integrate over the monoidal unit and such that the Fundamental Theorems of Calculus hold. We also show that generalizations of the relational model (which are biproduct completions of complete semirings) are also differential linear categories with antiderivatives.
Research and Application of Urban Traffic Survey Method Based on Commercial Video Satellite Remote Sensing Technology

Aiming at the technical requirements of modern urban traffic survey for macroscopic, visual, and dynamic monitoring, this paper proposes a method for monitoring and analyzing urban traffic based on commercial video satellite and intelligent image processing technology. Firstly, the video satellite-based monitoring method was introduced based on the surveillance ability analysis of the commercial video satellites. Then, the calculation method of the traffic density, speed, and flow based on the video satellite data is developed. Finally, a case study of eastern area of Changchun City was performed. 11×4.5 square kilometer of urban traffic was observed by video satellite. The work of data acquisition, information extraction, and traffic analysis were conducted within a week and the labor costs were reduced greatly by using the intelligent image processing technology; moreover, the traffic flow calculation errors are reduced by 10% compared with the highway traffic flow statistical data.
Personalized Newspaper Based on Emotional Traits Using Machine Learning

News has become an important part of our lives. It comes in different forms, right from morning newspaper to online pop ups while surfing on net. News presented to us in sporadic fashion. Presenting personalized news based on individual's likings, mood and emotional preferences is need of time. News unknowingly creates desired or undesired psychological impact. Right from title, presentation and sequencing to filtering and personalization - machine learning and cognitive sciences can play a key role in news computing and processing. This paper looks into research carried out in the area of cultural and news computing in elaborate way and proposes a model for personalized sequencing and presentation of news. This personalized newspaper aims to present news of your liking and suitable to your emotional makeup so that the overall desired impact will be achieved. The personalization will take care of personal context, emotional context along with individuals likings. Further presentation of news considering emotional make-up can help in producing overall positive impact.
Subjectively Experienced Time in Human-Computer Interaction: The Role of Passage of Time in User Experience

A study by Sackett et al. (2010) indicates that participants attribute more fun to an activity when they have the impression that time flies by. We investigate if this effect also occurs in human-computer interaction and impacts on User Experience (UX). We induced differences in the subjective passage of time by dividing the participants (n = 61) into three groups. Although all participants performed the same tasks for 10 min, the groups received different information about the duration of their activities (10, 20, or 5 min). This manipulation should invoke the impression that time passed normally, fast or slowly. Results showed that only a feeling of time passing by slowly could be induced. This temporal illusion led to a lower perceived usability and a lower overall UX while attractiveness and valence were not affected. We conclude that the investigation of subjectively experienced time improves the understanding of UX.
Toward Programmable Interdomain Routing

End-to-end route control spanning a set of autonomous systems (ASes) can provide opportunities to both end users to optimize interdomain control and network service providers to increase business offerings. BGP, the de facto interdomain routing protocol, and recent interdomain proposals provide limited mechanisms for such control. We provide the first, systematic formulation of the software-defined internetworking (SDI) model, where an AS exposes a programmable interface to allow clients to define the interdomain routes of the network, and maintains its autonomy, by keeping the control of its export policies, to avoid fundamental violations such as valley routing. We develop a blackbox optimization algorithm to quickly find optimal export-policy-compliant end-to-end routes in SDI, and validate its efficacy using real interdomain topology. To understand the operational implication of SDI, we evaluate the privacy leakage brought by exposing an AS' available interdomain routes. Preliminary results show that a small number of neighbors or a large number of exposed RIB samples allows accurate inference on an AS' BGP selection policy, indicating a potential risk of not only SDI, but the whole interdomain routing community.1
Affective Sequences and Student Actions Within Reasoning Mind

Now that the modeling of affective states is beginning to mature, understanding affect dynamics has become an increasingly realistic endeavor. However, the results from empirical studies have not always matched those of theoretical models, which raises questions as to why. In this study, we explore the relationship between affective sequences that have been previously explored in the literature and the activities students may engage in when interacting with Reasoning Mind, a blended learning system for elementary mathematics. The strongest correlations are found for students who shift from engaged concentration to frustration, making fewer actions in the system. While confusion is generally associated with positive patterns, and frustration and boredom have unexpectedly similar implications for student activity.
Designing Mobile-apps for context-aware notification: case study Merapi Volcano

Abstract The need for disaster preparedness becomes very important in Indonesia. Due to its geographical situation, within a ring of fire, Indonesia is prone to several disasters such as earth-quake and volcano eruption. This paper presents the design of a context-aware system to pre-emptively notify and provide some information to the population around Merapi volcano. The information contains some dangerous area and safe-place/shelter based on the user location. The proposed design can also notify the level of danger that might be encountered by the user and the mitigation path within a certain distance.
Optimized Quantum Implementation Approach

Complex computing environment is switching from peta scale to exa scale. Conventional irreversible logic will not be going to sustain forever and power dissipation need to be controlled. Lot of research is going on to avoid power dissipation due to switching but only a few research efforts have been applied on power dissipation due to loss of bits. Reversible computing is found to be one of the emerging and promising technologies to overcome addressed power dissipation due to loss of bits. Quantum cost is one of important metric of comparison and required further for analyzing performance of one reversible logic based circuit over other. This paper proposes the algorithm to draw optimized quantum implementation of reversible logic gates and further utilized for investigation of quantum cost. The optimized quantum cost obtained from proposed method is verified using RCViewer+ tool.
Steganalysis Method of Static JPEG Images Based on Artificial Immune System

This work aims to develop the heuristic steganalysis method of static JPEG images, based on the usage of artificial immune systems that allows detecting the presence of hidden information in them with good results on image processing time. A formal description of the artificial immune system’s primary nodes and an analysis of the obtained experimental results are presented. The proposed method allows detecting the presence of hidden information embedded by various popular steganography tools (like OutGuess, Steghide, and F5) in static JPEG images with sufficiently high accuracy.
Research on the User Experience of Educational App in the Context of “Intangible Cultural Heritage”

In the new era, education as an important way to inherit intangible cultural heritage needs to be combined with APPs. In this context, this article studies the user experience of the handicraft education app by experiment. Experimental results show that user experience and user willingness affect learning outcomes. And through TAM model and user experience knowledge, the factors affecting user will and experience are discussed. It also puts forward design suggestions for a craft education app in the context of intangible cultural heritage.
Lightweight Comprehensive Evaluation Method for Wireless User Perception Based on Random Forest

Comprehensive evaluation methods of wireless user perception for cellular cells in the same scenario involve multi-indicators. Traditional methods use the weighted sum of all indicators as the evaluation result. However, many unimportant indicators occupy a part of the overall weight, which leads to an unconvincing evaluation result. To achieve a convincing and accurate result, we propose a lightweight comprehensive evaluation method. Firstly, most important indicators are chosen via the random forest algorithm. Secondly, those indicators are weighted via the entropy method. Finally, we compute the score of all cells with the weights. Experiment results are given to show that the cells with higher scores perform better in all indicators, which is coincide with the actual situation. Hence, our proposed method is not only lightweight but also obtain a more accurate result.
Classification of Infection Type Based on Leukocytes Examination Results Using K-Nearest Neighbor

Abstract Leukocytes are blood cells that contain nuclei, also called white blood cells. Leukocytes have a role in the cellular and humoral defiance of organisms against foreign substances. Laboratory tests of blood samples greatly influence the diagnosis of a disease. Manual blood tests do have a low price but still have some weaknesses such as the length of time needed will be longer, because health practitioners must examine them carefully to avoid misinformation. To help overcome these weaknesses, a classification of types of infections was carried out based on the results of leukocyte examination. Classification is a grouping of data where the data used has a label or target class. So that the algorithms for solving classification problems are categorized into supervised learning. The purpose of supervised learning is that label data or targets play a role as a ‘supervisor’ or ‘teacher’ who oversees the learning process in achieving a certain level of accuracy or precision. The algorithm used in this study is K-Nearest Neighbour. The data used in this study as many as 2,098 results of complete blood tests taken from one hospital in Medan. This study resulted in a classification accuracy of 92%.
Multi Resolution Analysis compared Savitzky - Golay and Moving Average Filter by Noise Reduction

The aim of this paper is to offer an alternative signal analysis using Multiresolution Analysis (MRA), with the practical purpose of analyzing its performance in reducing the noise components. In a first part, a brief theoretical basis is shown for the Wavelets theory for signal analysis. Subsequently, simulations are carried out to reduce the noise through the multiresolution analysis of the Discrete Wavelet Transform and the they are compared with the results obtained through typical filters, such as Savitzky-Golay and Moving Average Filter.
A cyber-physical environment for detecting exceptional and dangerous human behavior in the home by sensors and its verification by computer simulation

Today aging of society is becoming increasingly important. Many people want to live at home as long as possible. Very often this leads to accidents at home, which are unnoticed for a long time and therefore cause severe complications. We propose a cyber-physical system consisting of sensors for motion, light, temperature, and so on, which monitors the behavior of elderly people but is non-invasive in the sense that the persons are not observed by a camera and must not wear certain sensors on their body. Unusual behavior or accidents are registered in-time by a change point detection algorithm based on Markov chains, which does not store any data with the exception of the last datum, so that also the integrity of the elderly people is preserved. For verification of this algorithm, a cyber-physical test environment has been programmed which allows to simulate human common and uncommon behavior in house during day and night. This simulation environment is based on behavior trees and decision theory. The verification results suggest that the implemented cyber-physical system for change point and anomaly detection could be successfully used in the real environment of elderly people to give them help in-time when an accident occurs.