Reviewerly/exHarmony huggingface dataset:
The development of IoT-based non-obstructive monitoring system for human's sleep monitoring

During the past few years, Internet of Things (IoT) has unprecedentedly become a buzz word in most areas of business and industry. In this paper, we have developed an IoT solution for human's sleep monitoring based on a data pillow system over the internet via 3G/4G LTE or internet router. A private java application server displays the sleeping information via graphs and widgets together with Node-Red which is a platform on android and iOS mobile apps. To observe sleep information, we have adopted force sensitive resistors (FSRs) installed under the pillow for collecting breathing data. A small single board computer, LattePanda, was used as data acquisition and management system. Initial test results showed that the data pillow successfully to upload sleep data to the server smoothly. The analysis of raw sleep data shows that the IoT data pillow can detecting difference breathing signals between normal respiration, hypopnea and apnea. The integration of sensors, wireless technology, and IoT could improve the data pillow system in terms of facilitating the users to measure their own sleep quality at home while the doctor can access to sleep data of patients over the internet.
Wave front phase imaging of wafer geometry using high pass filtering to reveal nanotopography

In this paper we show that Wave Front Phase Imaging (WFPI) has high lateral resolution and high sensitivity enabling it to measure nanotopography and roughness on a silicon wafer by simply acquiring a single image of the entire wafer. WFPI is achieved by measuring the reflected light intensity from monochromatic uncoherent light at two different planes along the optical path with the same field of view. We show that the lateral resolution in the current system used for these experiments is 24μm but can be pushed to less than 5μm by simply adding more pixels to the image sensor, and that the amplitude resolution limit is 0.3nm. Three 2-inch unpatterned silicon wafers were measured, and the nanotopography and roughness was revealed by applying a double Gaussian high pass filter to the global topography data.
Sampling on Graphs: From Theory to Applications.

The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review current progress on sampling over graphs focusing on theory and potential applications. Although most methodologies used in graph signal sampling are designed to parallel those used in sampling for standard signals, sampling theory for graph signals significantly differs from the theory of Shannon--Nyquist and shift-invariant sampling. This is due in part to the fact that the definitions of several important properties, such as shift invariance and bandlimitedness, are different in GSP systems. Throughout this review, we discuss similarities and differences between standard and graph signal sampling and highlight open problems and challenges.
Dynamic layout optimization for multi-user interaction with a large display

In this paper, we propose a user interface that allows multiple users to obtain information interactively and to operate the interface simultaneously on a large display. Users can interact with the display using hand gestures, which enables many users to easily use the system from a distance. In order for each user to effectively use the screen and also not to be interfered with by other users, the system dynamically optimizes the layout of the screen. We created a prototype system and conducted an experiment to compare the proposed interface with those without optimization. The results showed that the participants were able to obtain more information per unit time using the proposed interface. Moreover, the participants were less hurried or frustrated, and felt less interfered with by other users.
Demonstrating Personalized Multifaceted Visualization of People Recommendation to Conference Participants

We demonstrate an intelligent, personalized, multifaceted visualization of people recommendation using a personalized 2D entities graph and a word cloud for exploration by the user. This visualization aims to show non-trivial connections, e.g., those that the user may had forgotten about, but they are interesting and relevant. Since entities we are linked to are part of our lives (and profile), they help to understand who we are and what are we interested in. We adapt the typed entity-relation graph (profile) concept as introduced by [1] and based on this presentation we visualize the entity profile. In this demonstration, the users, as case study are the participants of IUI'20, will be able to explore their own personalized entities graph based on entities and relations that the system harvest about them (after getting their approval), from the web for finding interesting connections that they may meet in the context of this conference.
Realistic Handwriting Generation Using Recurrent Neural Networks and Long Short-Term Networks

Generating human-like handwriting by machine from an input text given by the user may seem as an easy task but is very complex in reality. It might not be possible for every human being to write in perfect cursive handwriting because each letter in cursive gets shaped differently depending on what letters surround it, and everyone has a different style of writing. This makes it very difficult to mimic a person’s cursive style handwriting with the help of a machine or even by hand for a matter of fact. This is why signing names in cursive is preferable on any legal documents. In this paper, we will try to use various deep learning methods to generate human-like handwriting. Algorithms using neural networks enable us to achieve this task, and hence, recurrent neural networks (RNN) have been utilized with the aim of generating human-like handwriting. We will discuss the generation of realistic handwriting from the IAM Handwriting Database and check the accuracy of our own implementation. This feat can be achieved by using a special kind of recurrent neural network (RNN), the Long Short-Term Memory networks (LSTM).
A flexible <i>n</i>/2 adversary node resistant and halting recoverable blockchain sharding protocol

Blockchain sharding is a promising approach to solving the dilemma between decentralisation and high performance (transaction throughput) for blockchain. The main challenge of Blockchain sharding systems is how to reach a decision on a statement among a sub-group (shard) of people while ensuring the whole population recognises this statement. Namely, the challenge is to prevent an adversary who does not have the majority of nodes globally but have the majority of nodes inside a shard. Most Blockchain sharding approaches can only reach a correct consensus inside a shard with at most $n/3$ evil nodes in a $n$ node system. There is a blockchain sharding approach which can prevent an incorrect decision to be reached when the adversary does not have $n/2$ nodes globally. However, the system can be stopped from reaching consensus (become deadlocked) if the adversary controls a smaller number of nodes. In this paper, we present an improved Blockchain sharding approach that can withstand $n/2$ adversarial nodes and recover from deadlocks. The recovery is made by dynamically adjusting the number of shards and the shard size. A performance analysis suggests our approach has a high performance (transaction throughput) while requiring little bandwidth for synchronisation.
Assessing Regression-Based Sentiment Analysis Techniques in Financial Texts

Sentiment analysis (SA) is increasing its importance due to the enormous amount of opinionated textual data available today. Most of the researches have investigated different models, feature representation and hyperparameters in SA classification tasks. However, few studies were conducted to evaluate the impact of these features on regression SA tasks. In this paper, we conduct such assessment on a financial domain data set by investigating different feature representations and hyperparameters in two important models -- Support Vector Regression (SVR) and Convolution Neural Networks (CNN). We conclude presenting the most relevant feature representations and hyperparameters and how they impact outcomes on a regression SA task.
SpectreRewind: A Framework for Leaking Secrets to Past Instructions.

Transient execution attacks utilize micro-architectural covert channels to leak secrets that should not have been accessible during logical program execution. Commonly used micro-architectural covert channels are those that leave lasting footprints in the microarchitectural state, for example, a cache state change, from which the secret is recovered after the transient execution is completed. In this paper, we present SpectreRewind, a new approach to create contention based covert channels for transient execution attacks. In our approach, a covert channel is established by issuing the necessary instructions logically before the transiently executed victim code. Unlike prior contention based covert channels, which require simultaneous multi-threading (SMT), SpectreRewind supports single hardware thread based covert channels, making it viable on systems where attacker cannot utilize SMT. We show that contention on the floating point division unit on commodity processors can be used to create a high-performance (~100 KB/s), low-noise covert channel for transient execution attacks instead of commonly used flush+reload based cache covert channels. We implement a Meltdown attack utilizing the proposed covert channel showing competitive performance compared to the stateof-the-art cache based covert channel implementation. We also show that the covert channel works in the JavaScript engine of a Chrome browser.
Content-Aware Successive Point-of-Interest Recommendation

Successive point-of-interest (POI) recommendation based on user check-in histories plays an important role in mobile-based social media platforms.Although a large amount of check-in data including textual content is generated from such platforms, most successive POI recommendation models do not leverage textual contents that provide useful information for understanding user interests.To address this problem, we propose a new content-aware successive POI recommendation (CAPRE) model in this paper.Based on a multi-head attention mechanism and a character-level convolutional neural network, CAPRE encodes usergenerated textual contents into content embedding to capture user interests.Based on long short-term memories (LSTMs), CAPRE capture content-aware user behavior patterns from encoded content embedding.Evaluation results on real-world datasets show that CAPRE achieves state-of-the-art recommendation performance.
The Development of Learning Media of Pakakala Boardgame

The purpose of this study is to develop a learning media in the form of boardgame Learning Media (Optional Board and Career Cards) to improve studentsâ€™ career knowledge in a fun, interactive and active learning under. This research uses Research and Development Method, with The phases in this research consist of; (1) product analysis, (2) developing products, (3) validation and revision, (4) play-trials and final products. Data collection techniques using interviews and questionnaires. Feasibility check and assessment by theory expert, learning media expert and users using Likert scale with the value used are 5 to 1. The results of the validation by theory expert get score 5 with a very worthy category, while the validation by media expert gets score 4.89 with the very worthy category. Whereas in the usage test by users or students and teacher response as an education practitioner showed very worthy category with scores 4.53 and 5 respectively. As a result, the developed pakakala boardgame for year 4 students at SDN 84 Parepare is suitable to use in teaching theme 4 concerning various occupations
A Context-Aware Security Model for a Combination of Attribute-Based Access Control and Attribute-Based Encryption in the Healthcare Domain

The need of a trusted environment in which only authorized users are permitted to access a system was of imperative importance since the early days of cloud computing. Even nowadays, a lot of users seem to be reluctant to store their personal data in the cloud and specifically the data related to bank accounts and the health care domain. Our goal is to enhance the access control mechanisms that can be used in the healthcare domain for enhancing the security and privacy of EHR systems. In this work, we present a context-aware security model which consists of classes and properties that can serve as background knowledge for creating and enforcing access control rules for electronic health records (EHR). We consider two different layers of authorization control based on the current context: (i) the Attribute Based Access Control (ABAC) layer which permits or denies access and/or editing rights to (encrypted) EHRs; and (ii) the Attribute Based Encryption (ABE) layer which handles the way sensitive data should be decrypted.
Time crystals enter the real world of condensed matter

Time crystals break time-translational symmetry rather than spatial symmetry, as ordinary crystals do. Peter Hannaford and Krzysztof Sacha look at how this exotic state could have similar applications to condensed-matter devices
Flow driven attention network for video salient object detection

Salient object detection has been revolutionised by convolutional neural network (CNN) recently. However, it is hard to transfer the state-of-the-art still-image based saliency detectors to videos directly, owing to the neglect of temporal contexts between frames. In this study, the authors propose a flow-driven attention network (FDAN) to exploit motion information for video salient object detection. FDAN consists of an appearance feature extractor, a motion-guided attention module and a saliency map regression module. It extracts the appearance feature per frame, refines appearance feature with optical flow and infers the ultimate saliency map, respectively. Motion-guided attention module is the core of FDAN, which extracts motion information in the form of attention. This attention mechanism is a two-branch CNN, fusing optical flow and appearance features. In addition, a shortcut connection is applied to the attention multiplied feature map for noise suppression intensively. Experimental results show that the proposed method can achieve performance on par with the state-of-the-art method flow-guided recurrent neural encoder on challenging benchmarks of Densely Annotated Video Segmentation and Freiburg–Berkeley Motion Segmentation while being two times faster in detection.
An advanced solutions for operators’ training working in confined and /or pollution suspected space

Carrying out maintenance, regulation, installation of mechanical parts, cleaning and other activities in confined and / or suspected pollution spaces, often represents a challenge; indeed many are the cases of fatal incidents for operators. As in every country, there are ad-hoc regulations to increase the safety of operators at work: as far as Italy is concerned, the decree 177/2011 requires the qualification of companies operating in confined and / or suspected of pollution spaces with the aim of raising the level of safety, also defining strict criteria for all those business contracts related to confined spaces. However, while regulations clearly define what confined spaces are, the training issues are not appropriately addressed (e.g. the Italian legislation only makes reminder to general laws such as the decree 81/2008) and there is no uniformity in terms of methods, procedures and required training times for operators working in confined spaces. The Italian National Institute for Insurance against Accidents at Work (INAIL) has conceptualized and developed a physical simulator for training those operators working in confined spaces. The research activities carried out by using the physical simulator points out that the integration of Industry 4.0 enabling technologies (such as Virtual and Augmented Reality developed in cooperation with University of Calabria) may further extend the training capabilities and operators’ skills. The idea proposed in this article is to show how live exercises made with a physical simulator combined with Virtual Immersive and Interactive Reality can be profitably used to tackle the operators’ training problem within confined spaces.
A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups.These groups have a little number of users: relatives, friends, colleagues, etc.Our proposal deals with large numbers of automatically detected groups.Marketing and electronic commerce are typical targets of large homogenous groups.Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations.We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings.Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation.Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim.We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information.Results show an improvement on recommendations made to large homogeneous groups.It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups.
ACO based comprehensive model for software fault prediction

The comprehensive models can be used for software quality modelling which involves prediction of low-quality modules using interpretable rules. Such comprehensive model can guide the design and testing team to focus on the poor quality modules, thereby, limited resources allocated for software qual ity inspection can be targeted only towards modules that are likely to be defective. Ant Colony Optimization (ACO) based learner is one potential way to obtain rules that can classify the software modules faulty and not faulty. This paper investigates ACO based mining approach with ROC based rule quality updation to constructs a rule-based software fault prediction model with useful metrics. We have also investigated the effect of feature selection on ACO based and other benchmark algorithms. We tested the proposed method on several publicly available software fault data sets. We compared the performance of ACO based learning with the results of three benchmark classifiers on the basis of area under the receiver operating characteristic curve. The evaluation of performance measure proves that the ACO based learner outperforms other benchmark techniques.
Reliable Holoscopic 3D Face Recognition

Face recognition has made significant progress in recent years. It has been successfully applied to many applications from security entrance systems for staff identification to visual surveillance. However, the performance of current systems are highly affected by factors such as subject's head orientation, facial expressions, and face wear-on. In order to address this challenge, researchers usually resort to more training data or more complex recognition models. We believe the secret lies in using a more rich data source. Therefore, in this paper, we use a holoscopic 3D (H3D) imaging system. To show the effectiveness of the sensor, we use it with a simple back propagation neural network (BPNN) classifier. We illustrate the accuracy of the system through an experiment and show that it has high accuracy and is robust against challenges aforementioned. The proposed approach consists of three steps: (a) H3D face images are captured using the single aperture H3D camera. (b) Wavelet features are extracted and selected from multiperspective viewpoint images obtained from the H3D images. (c) A simple artificial neural network (ANN) is used for reliable face recognition.
Maximum entanglement of formation for a two-mode Gaussian state over passive operations

We quantify the maximum amount of entanglement of formation (EoF) that can be achieved by continuous-variable states under passive operations, which we refer to as the EoF potential. Focusing, in particular, on two-mode Gaussian states we derive analytical expressions for the EoF potential for specific classes of states. For more general states, we demonstrate that this quantity can be upper bounded by the minimum amount of squeezing needed to synthesize the Gaussian modes, a quantity called squeezing of formation. Our work, thus, provides a link between nonclassicality of quantum states and the nonclassicality of correlations.
Contrastive Learning for Lifted Networks.

In this work we address supervised learning via lifted network formulations. Lifted networks are interesting because they allow training on massively parallel hardware and assign energy models to discriminatively trained neural networks. We demonstrate that training methods for lifted networks proposed in the literature have significant limitations, and therefore we propose to use a contrastive loss to train lifted networks. We show that this contrastive training approximates back-propagation in theory and in practice, and that it is superior to the regular training objective for lifted networks.
Quality of Experience Assessment of 360-degree video

The research domain on the Quality of Experience (QoE) of 2D video streaming has been well established. However, a new video format is emerging and gaining popularity and availability: VR 360-degree video. The processing and transmission of 360-degree videos brings along new challenges such as large bandwidth requirements and the occurrence of different distortions. The viewing experience is also substantially different from 2D video, it offers more interactive freedom on the viewing angle but can also be more demanding and cause cybersickness. Further research on the QoE of 360-videos specifically is thus required. The goal of this study is to complement earlier research by (Tran, Ngoc, Pham, Jung, and Thank, 2017) testing the effects of quality degradation, freezing, and content on the QoE of 360-videos. Data will be gathered through subjective tests where participants watch degraded versions of 360-videos through an HMD. After each video they will answer questions regarding their quality perception, experience, perceptual load, and cybersickness. Results of the first part show overall rather low QoE ratings and it decreases even more as quality is degraded and freezing events are added. Cyber sickness was found not to be an issue.