Reviewerly/exHarmony huggingface dataset:
Detachable Smartwatch

Glanceability and low access time are arguably the key assets of a smartwatch. However, smartwatches are currently limited to micro-interactions. They do not enable complex interactions and, in general, they do not afford continuous use for long. We believe that smartwatches can retain micro-interactions and glanceability, but also get better at long and complex interactions. We propose a smartwatch that a user can detach, and use as more than a wearable depending on their context, requirements, and preference. Detaching the watch enables it to morph into different forms, and thereby become a better interaction device, better display, and a better sensor suite. First, we interview participants to elicit usage themes for a detachable watch. Then, we build applications that showcase the range of use-cases where a detachable smartwatch offers additional functionality compared to an always-worn one, and highlights the affordances and benefits enabled due to detachability.
Sensor Data Compression Using Bounded Error Piecewise Linear Approximation with Resolution Reduction

Smart production as one of the key issues for the world to advance toward Industry 4.0 has been a research focus in recent years. In a smart factory, hundreds or even thousands of sensors and smart devices are often deployed to enhance product quality. Generally, sensor data provides abundant information for artificial intelligence (AI) engines to make decisions for these smart devices to collect more data or activate some required activities. However, this also consumes a lot of energy to transmit the sensor data via networks and store them in data centers. Data compression is a common approach to reduce the sensor data size so as to lower transmission energies. Literature indicates that many Bounded-Error Piecewise Linear Approximation (BEPLA) methods have been proposed to achieve this. Given an error bound, they make efforts on how to approximate to the original sensor data with fewer line segments. In this paper, we furthermore consider resolution reduction, which sets a new restriction on the position of line segment endpoints. Swing-RR (Resolution Reduction) is then proposed. It has O(1) complexity in both space and time per data record. In other words, Swing-RR is suitable for compressing sensor data, particularly when the volume of the data is huge. Our experiment results on real world datasets show that the size of compressed data is significantly reduced. The energy consumed follows. When using minimal resolution, Swing-RR has achieved the best compression ratios for all tested datasets. Consequently, fewer bits are transmitted through networks and less disk space is required to store the data in data centers, thus consuming less data transmission and storage power.
Wearable Health

The large number of sensors and form factors available enhance the potential of wearables to support, assist and facilitate healthcare. From activity trackers that aid to prevent diseases and monitor patients' conditions to assistive devices that provide therapeutic support, numerous conditions benefit from continuous data collection and long-term analysis. The benefits involve patients, and help to inform and to improve standard care practices thanks to a better understanding of the patients' conditions and responses to treatments and interventions. Wearable sensors allow objective measures of data, being less error-prone, more accurate that self-reports and more consistent than data manually annotated. While advances in storage, processing power, and battery life raised the number of applications in wearable health, such advances did not come without drawbacks. The potential of wearable health has been increasing with the growth in research and development in the field. However, many open challenges to be addressed still exist to ensure that applications are effective and safe to use before mainstream adoption. Key challenges that must be addressed to reduce risks are related to the accuracy of the data collection, performance of algorithms for data analysis, patients' safety and privacy protection. This paper discusses the design space for wearable health, listing form factors, sensors and applications. It also highlights opportunities and challenges in the domain.
On the Upload versus Download Cost for Secure and Private Matrix Multiplication

In this paper, we study the problem of secure and private distributed matrix multiplication. Specifically, we focus on a scenario where a user wants to compute the product of a confidential matrix $A$, with a matrix $B_\theta$, where $\theta\in\{1,\dots,M\}$. The set of candidate matrices $\{B_1,\dots,B_M\}$ are public, and available at all the $N$ servers. The goal of the user is to distributedly compute $AB_{\theta}$, such that $(a)$ no information is leaked about the matrix $A$ to any server; and $(b)$ the index $\theta$ is kept private from each server. Our goal is to understand the fundamental tradeoff between the upload vs download cost for this problem. Our main contribution is to show that the lower convex hull of following (upload, download) pairs: $(U,D)=(N/(K-1),(K/(K-1))(1+(K/N)+\dots+(K/N)^{M-1}))$ for $K=2,\dots,N$ is achievable. The scheme improves upon state-of-the-art existing schemes for this problem, and leverages ideas from secret sharing and coded private information retrieval.
Intelligent Health Monitoring Systems for Space Missions Based on Data Mining Techniques

Development of intelligent health monitoring system for artificial satellites is one of the important issue of aerospace engineering, in which determines the health state and failure prediction of satellites based on the telemetry data. Recent development in data mining techniques make it possible to examine satellite telemetry data and extract embedded information to produce advanced system health monitoring applications. This study presents a framework of the essential operations and applications of intelligent health monitoring systems which are being applied in the ground control station. Furthermore, this study reviews an extensive collection of existing health monitoring solutions and discusses them in a framework of telemetry data mining techniques. The work presented in this study can be used as a guideline for designing intelligent health monitoring solution based on telemetry data mining techniques.
Forensic Analysis using Text Clustering in the Age of Large Volume Data: A Review

Exploring digital devices in order to generate digital evidence related to an incident being investigated is essential in modern digital investigation.The emergence of text clustering methods plays an important role in developing effective digital forensics techniques.However, the issue of increasing the number of text sources and the volume of digital devices seized for analysis has been raised significantly over the years.Many studies indicated that this issue should be resolved urgently.In this paper, a comprehensive review of digital forensic analysis using text-clustering methods is presented, investigating the challenges of large volume data on digital forensic techniques.Moreover, a meaningful classification and comparison of the text clustering methods that have been frequently used for forensic analysis are provided.The major challenges with solutions and future research directions are also highlighted to open the door for researchers in the area of digital forensics in the age of large volume data.
UASOL, a large-scale high-resolution outdoor stereo dataset

In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite there already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. Our dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data, as shown in this work.
KRİPTO PARA BİTCOİN ve DÖVİZ KURLARI İLİŞKİSİ: YAPISAL KIRILMALI EŞBÜTÜNLEŞME ve NEDENSELLİK ANALİZİ

Bitcoin, which is a virtual and crypto money, is a currency that includes digital transactions, technically expressed as block chains, and is not included in the central monetary system.The purpose of the current study is to investigate the relationship between crypto currency Bitcoin and exchange rates.The relationship between the exchange rates of the Euro, Japanese Yen, British Sterling, Australian Dollar, Canadian Dollar, Swiss Franc, Yuan Renminbi and Swedish Kron based on the daily exchange rate values for the period of 3.02.2012-04.10.2017 is investigated through Gregory and Hansen cointegration with structural breaks and Granger causality analysis.As a result of the analysis, structural breaks are seen in the BTC/USD exchange rate in April and December, 2013.Moreover, findings reveal a long-term cointegration relationship between the time series for exchange rates.
Prediction of Air Quality Based on KNN-LSTM

Since most of the existing air quality index (AQI) predicting models focused on prediction of the time series data of a single target monitoring station, they failed to consider the correlation and mutual influence among the air quality monitoring station sites and the spatio-temporal characteristics of air quality. And this will lead to a certain one-sidedness during air quality prediction of a particular site. Aimed at this problem, a short-term air quality prediction model based on K-nearest neighbor (KNN) and Long Short-Term Memory (LSTM) was proposed. The model firstly used KNN algorithm to select the time and space-related monitoring stations, then the air quality index sequences of these stations were constructed into data sets, followed by training and testing processes in the LSTM model, and finally the model was verified with real data. It is suggested that the prediction accuracy of the hybrid prediction model constructed in this paper is acceptable in terms of the space-time correlation, and it could be an alternative for further application of air quality prediction.
Estimation of Copy-sensitive Codes Using a Neural Approach

Copy sensitive graphical codes are used as anti-counterfeiting solution in packaging and document protection. Their security is funded on a design hard-to-predict after print and scan. In practice there exist different designs. Here random codes printed at the printer resolution are considered. We suggest an estimation of such codes by using neural networks, an in-trend approach which has however not been studied yet in the present context. In this paper, we test a state-of-the-art architecture efficient in the binarization of handwritten characters. The results show that such an approach can be successfully used by an attacker to provide a valid counterfeited code so fool an authentication system.
Technology-enhanced Self-directed Language Learning Behaviors of EFL Student Teachers

English as a Foreign Language (EFL) education not only requires students to receive in-class instruction but also necessitates them to engage in extra practices outside the classroom by using technological or non-technological resources. Hence, this study aimed to investigate the self-directed language learning behaviors of the EFL student teachers and their use of technology in the process by employing a mixed-methods study within an explanatory sequential research design. The quantitative data were collected from 110 EFL student teachers by using two distinct research instruments, whereas the qualitative data were collected through an open-ended questionnaire form, responded by 47 of these students. The results of the study indicated that technology utilization is a significant predictor of self-directed language learning. However, the findings show that the off-campus self-directed language learning abilities of the students, with or without the use of technology, do not significantly differ according to gender, age, and grade levels. The differences were only found in terms of unaided off-campus learning abilities of the students and their technology utilization frequency in language learning. The qualitative findings also show that students engage in a variety of self-directed language learning activities outside the classroom mostly by using technology.
Infrared Deflectometry for Slope Deformation Measurements

This paper presents an implementation of deflectometry in the infrared spectrum. Deflectometry consists in recording the specular image of a reference grid pattern onto the mirror-like surface of a test specimen. This technique has two main advantages, high sensitivity and direct measurement of surface slopes, which in the case of thin plate bending is only one spatial differentiation away from surface strains. The objective of imaging in the infrared spectrum is to mitigate the main limitation of deflectometry in the visible spectrum, which is to require an extremely smooth surface to provide dominant specular reflection. This paper explores IR deflectometry for the first time for deformation measurements. Two different infrared cameras were assessed for use in IR deflectometry, a short wave quantum detector one, and a long wave microbolometer (MB) array one. Different materials of varying surface roughness were imaged and it was verified that the Rayleigh criterion was appropriate to determine whether IR deflectometry was feasible on a given surface. With the MB camera, most off-the-shelf material surfaces proved reflective enough to perform IR deflectometry. Finally, several bending tests were performed on aluminium plates and the deformation fields were shown to compare remarkably well with finite element simulations. The experimental data were then used in the Virtual Fields Method (VFM) and the elastic stiffness components of aluminium were retrieved with excellent accuracy, further validating IR deflectometry.
Multi-Speaker End-to-End Speech Synthesis

In this work, we extend ClariNet (Ping et al., 2019), a fully end-to-end speech synthesis model (i.e., text-to-wave), to generate high-fidelity speech from multiple speakers. To model the unique characteristic of different voices, low dimensional trainable speaker embeddings are shared across each component of ClariNet and trained together with the rest of the model. We demonstrate that the multi-speaker ClariNet outperforms state-of-the-art systems in terms of naturalness, because the whole model is jointly optimized in an end-to-end manner.
MagicPAPER

This study proposes a tabletop projection device that can be implemented by combining physical objects with interactive projections. Users can interact on kraft papers using daily tools, such as marker pens, toothbrushes, colored blocks, and square wooden blocks. The input of the proposed device is a multifunction sensor, and the output is a tabletop projector. Using MagicPAPER, four types of interactions are implemented, namely drawing, gesture recognition, brushing, and building blocks. The abstract and poster discuss the design motivations and system descriptions of MagicPAPER.
LEDAcrypt: QC-LDPC Code-Based Cryptosystems with Bounded Decryption Failure Rate

We consider the QC-LDPC code-based cryptosystems named LEDAcrypt, which are under consideration by NIST for the second round of the post-quantum cryptography standardization initiative. LEDAcrypt is the result of the merger of the key encapsulation mechanism LEDAkem and the public-key cryptosystem LEDApkc, which were submitted to the first round of the same competition. We provide a detailed quantification of the quantum and classical computational efforts needed to foil the cryptographic guarantees of these systems. To this end, we take into account the best known attacks that can be mounted against them employing both classical and quantum computers, and compare their computational complexities with the ones required to break AES, coherently with the NIST requirements. Assuming the original LEDAkem and LEDApkc parameters as a reference, we introduce an algorithmic optimization procedure to design new sets of parameters for LEDAcrypt. These novel sets match the security levels in the NIST call and make the C99 reference implementation of the systems exhibit significantly improved figures of merit, in terms of both running times and key sizes. As a further contribution, we develop a theoretical characterization of the decryption failure rate (DFR) of LEDAcrypt cryptosystems, which allows new instances of the systems with guaranteed low DFR to be designed. Such a characterization is crucial to withstand recent attacks exploiting the reactions of the legitimate recipient upon decrypting multiple ciphertexts with the same private key, and consequentially it is able to ensure a lifecycle of the corresponding key pairs which can be sufficient for the wide majority of practical purposes.
Simultaneous Registration and Clustering for Multidimensional Functional Data

The clustering for functional data with misaligned problems has drawn much attention in the last decade. Most methods do the clustering after those functional data being registered and there has been little research using both functional and scalar variables. In this article, we propose a simultaneous registration and clustering model via two-level models, allowing the use of both types of variables and also allowing simultaneous registration and clustering. For the data collected from subjects in different groups, a Gaussian process functional regression model with time warping is used as the first level model; an allocation model depending on scalar variables is used as the second level model providing further information over the groups. The former carries out registration and modeling for the multidimensional functional data (two-dimensional curves) at the same time. This methodology is implemented using an EM algorithm, and is examined on both simulated data and real data. Supplementary materials for this article are available online.
Mesh: A Supply Chain Solution with Locally Private Blockchain Transactions

Abstract A major line of research on blockchains is geared towards enhancing the privacy of transactions through anonymity using generic non-interactive proofs. However, there is a good cluster of application scenarios where complete anonymity is not desirable and accountability is in fact required. In this work, we utilize non-interactive proofs of knowledge of elliptic curve discrete logarithms to present membership and verifiable encryption proof, which offers plausible anonymity when combined with the regular signing process of the blockchain transactions. The proof system requires no trusted setup, both its communication and computation complexities are linear in the number of set members, and its security relies on the discrete logarithm assumption. As a use-case for this scenario, we present Mesh which is a blockchain-based framework for supply chain management using RFIDs. Finally, the confidentiality of the transacted information is realized using a lightweight key chaining mechanism implemented on RFIDs. We formally define and prove the main security features of the protocol, and report on experiments for evaluating the performance of the modified transactions for this system.
Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL

Convolutional neural networks (CNNs) are the state-of-the-art method for most computer vision tasks. But, the deployment of CNNs on mobile or embedded platforms is challenging because of CNNs' excessive computational requirements. We present an end-to-end neural network solution to scene understanding for robot soccer. We compose two key neural networks: one to perform semantic segmentation on an image, and another to propagate class labels between consecutive frames. We trained our networks on synthetic datasets and fine-tuned them on a set consisting of real images from a Nao robot. Furthermore, we investigate and evaluate several practical methods for increasing the efficiency and performance of our networks. Finally, we present RoboDNN, a C++ neural network library designed for fast inference on the Nao robots.
Towards a General Abstract Meaning Representation Corpus for Brazilian Portuguese

Meaning Representation (AMR) is a recent and prominent semantic representation with good acceptance and several applications in the Natural Language Processing area.For English, there is a large annotated corpus (with approximately 39K sentences) that supports the research with the representation.However, to the best of our knowledge, there is only one restricted corpus for Portuguese, which contains 1,527 sentences.In this context, this paper presents an effort to build a general purpose AMR-annotated corpus for Brazilian Portuguese by translating and adapting AMR English guidelines.Our results show that such approach is feasible, but there are some challenging phenomena to solve.More than this, efforts are necessary to increase the coverage of the corresponding lexical resource that supports the annotation.
Security-Efficiency Tradeoffs in Searchable Encryption

Abstract Besides their security, the efficiency of searchable encryption schemes is a major criteria when it comes to their adoption: in order to replace an unencrypted database by a more secure construction, it must scale to the systems which rely on it. Unfortunately, the relationship between the efficiency and the security of searchable encryption has not been widely studied, and the minimum cost of some crucial security properties is still unclear. In this paper, we present new lower bounds on the trade-offs between the size of the client state, the efficiency and the security for searchable encryption schemes. These lower bounds target two kinds of schemes: schemes hiding the repetition of search queries, and forward-private dynamic schemes, for which updates are oblivious. We also show that these lower bounds are tight, by either constructing schemes matching them, or by showing that even a small increase in the amount of leaked information allows for constructing schemes breaking the lower bounds.
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion

Non-parallel multi-domain voice conversion (VC) is a technique for learning mappings among multiple domains without relying on parallel data. This is important but challenging owing to the requirement of learning multiple mappings and the non-availability of explicit supervision. Recently, StarGAN-VC has garnered attention owing to its ability to solve this problem only using a single generator. However, there is still a gap between real and converted speech. To bridge this gap, we rethink conditional methods of StarGAN-VC, which are key components for achieving non-parallel multi-domain VC in a single model, and propose an improved variant called StarGAN-VC2. Particularly, we rethink conditional methods in two aspects: training objectives and network architectures. For the former, we propose a source-and-target conditional adversarial loss that allows all source domain data to be convertible to the target domain data. For the latter, we introduce a modulation-based conditional method that can transform the modulation of the acoustic feature in a domain-specific manner. We evaluated our methods on non-parallel multi-speaker VC. An objective evaluation demonstrates that our proposed methods improve speech quality in terms of both global and local structure measures. Furthermore, a subjective evaluation shows that StarGAN-VC2 outperforms StarGAN-VC in terms of naturalness and speaker similarity. The converted speech samples are provided at http://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/stargan-vc2/index.html.