Reviewerly/exHarmony huggingface dataset:
FrownOnError: Interrupting Responses from Smart Speakers by Facial Expressions

In the conversations with smart speakers, misunderstandings of users' requests lead to erroneous responses. We propose FrownOnError, a novel interaction technique that enables users to interrupt the responses by intentional but natural facial expressions. This method leverages the human nature that the facial expression changes when we receive unexpected responses. We conducted a first user study (N=12) to understand users' intuitive reactions to the correct and incorrect responses. Our results reveal the significant difference in the frequency of occurrence and intensity of users' facial expressions between two conditions, and frowning and raising eyebrows are intuitive to perform and easy to control. Our second user study (N=16) evaluated the user experience and interruption efficiency of FrownOnError and the third user study (N=12) explored suitable conversation recovery strategies after the interruptions. Our results show that FrownOnError can be accurately detected (precision: 97.4%, recall: 97.6%), provides the most timely interruption compared to the baseline methods of wake-up word and button press, and is rated as most intuitive and easiest to be performed by users.
QAnswer KG: Designing a Portable Question Answering System over RDF Data

While RDF was designed to make data easily readable by machines, it does not make data easily usable by end-users. Question Answering (QA) over Knowledge Graphs (KGs) is seen as the technology which is able to bridge this gap. It aims to build systems which are capable of extracting the answer to a user's natural language question from an RDF dataset. In recent years, many approaches were proposed which tackle the problem of QA over KGs. Despite such efforts, it is hard and cumbersome to create a Question Answering system on top of a new RDF dataset. The main open challenge remains portability, i.e., the possibility to apply a QA algorithm easily on new and previously untested RDF datasets. In this publication, we address the problem of portability by presenting an architecture for a portable QA system. We present a novel approach called QAnswer KG, which allows the construction of on-demand QA systems over new RDF datasets. Hence, our approach addresses non-expert users in QA domain. In this paper, we provide the details of QA system generation process. We show that it is possible to build a QA system over any RDF dataset while requiring minimal investments in terms of training. We run experiments using 3 different datasets. To the best of our knowledge, we are the first to design a process for non-expert users. We enable such users to efficiently create an on-demand, scalable, multilingual, QA system on top of any RDF dataset.
Universal Lemmatizer: A sequence-to-sequence model for lemmatizing Universal Dependencies treebanks

Abstract In this paper, we present a novel lemmatization method based on a sequence-to-sequence neural network architecture and morphosyntactic context representation. In the proposed method, our context-sensitive lemmatizer generates the lemma one character at a time based on the surface form characters and its morphosyntactic features obtained from a morphological tagger. We argue that a sliding window context representation suffers from sparseness, while in majority of cases the morphosyntactic features of a word bring enough information to resolve lemma ambiguities while keeping the context representation dense and more practical for machine learning systems. Additionally, we study two different data augmentation methods utilizing autoencoder training and morphological transducers especially beneficial for low-resource languages. We evaluate our lemmatizer on 52 different languages and 76 different treebanks, showing that our system outperforms all latest baseline systems. Compared to the best overall baseline, UDPipe Future, our system outperforms it on 62 out of 76 treebanks reducing errors on average by 19% relative. The lemmatizer together with all trained models is made available as a part of the Turku-neural-parsing-pipeline under the Apache 2.0 license.
CurveBoards: Integrating Breadboards into Physical Objects to Prototype Function in the Context of Form

CurveBoards are breadboards integrated into physical objects. In contrast to traditional breadboards, CurveBoards better preserve the object's look and feel while maintaining high circuit fluidity, which enables designers to exchange and reposition components during design iteration. Since CurveBoards are fully functional, i.e., the screens are displaying content and the buttons take user input, designers can test interactive scenarios and log interaction data on the physical prototype while still being able to make changes to the component layout and circuit design as needed. We present an interactive editor that enables users to convert 3D models into CurveBoards and discuss our fabrication technique for making CurveBoard prototypes. We also provide a technical evaluation of CurveBoard's conductivity and durability and summarize informal user feedback.
A Low Power Circuit Design for Chaos-Key Based Data Encryption

Dynamic and non-linear systems have been used to generate random bits in high-security applications for decades. In this perspective, due to their stochastic characteristic, chaotic systems have been emerging as the natural choice for the generation of random bits. This paper presents the design and the implementation of a chaos-based true random number generator and a chaos-key based data encryption scheme for secure communications. The mathematical expression of the dynamic system is presented and analyzed to evaluate the possibility of chaos occurrence. Then, the chaotic system is realized at the circuit level using 130 nm CMOS technology to generate random bit sequences, which are utilized in data encryption. Chaotic signal outputs of the chaos-based random number generator circuit are sampled at a maximum frequency of 50 MHz, enabling a high throughput of random bits. The core of the chaotic circuit consumes $630~\mu \text{W}$ in static mode and a maximum of $660~\mu \text{W}$ in running mode. The chaos-based one-time pad encryption scheme using the chaos-key generator shows the advantages of using this random number generator in secure communications. In this context, the data secrecy is compared to the advanced encryption standard AES128. Moreover, the design is simulated in different working conditions such as voltage supply and temperature variations, where it is shown that the random bit output benefits from a high entropy per bit and passes the standard statistical test suite (NIST) for cryptographic applications.
"It's easier than causing confrontation": Sanctioning Strategies to Maintain Social Norms and Privacy on Social Media

Sanctions play an essential role in enforcing and sustaining social norms. On social networking sites (SNS), sanctions allow individuals to shape community norms on appropriate privacy respecting behaviors. Existing theories of privacy assume the use of such sanctions but do not examine the extent and effectiveness of sanctioning behaviors. We conducted a qualitative interview study of young adults (N=23), and extend research on collective boundary regulation by studying sanctions in the context of popular SNS. Through a systematization of sanctioning strategies, we find that young adults prefer to use indirect and invisible sanctions to preserve strong-tie relationships. Such sanctions are not always effective in helping the violator understand the nature of their normative violation. We offer suggestions on supporting online sanctioning that make norms more visible and signal violations in ways that avoid direct confrontation to reduce the risk of harming on-going social relationships.
Taguchi-TOPSIS based HOG parameter selection for complex background sign language recognition

This paper presents an approach to design Indian Sign Language (ISL) recognition system for complex background. In many applications, Histogram of Oriented Gradients (HOG) have been proved to be effective. However, it is observed that the choice of HOG parameters affects the feature vector size and its classification capability. The objective is to select the parameter values in order to have maximal accuracy at a minimal computational time and reduced feature vector size. A combined Taguchi and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based decision-making technique is applied to determine the values of these parameters. Results show that the combined TOPSIS-Taguchi based technique is effective in selecting the parameter combination to get high overall performance. For the acquired ISL complex background dataset, the selected values of parameters are further used to obtain multi-level HOG resulting in the overall accuracy of 92% for 280 features.
Expected Constant Round Byzantine Broadcast Under Dishonest Majority

Byzantine Broadcast (BB) is a central question in distributed systems, and an important challenge is to understand its round complexity. Under the honest majority setting, it is long known that there exist randomized protocols that can achieve BB in expected constant rounds, regardless of the number of nodes n. However, whether we can match the expected constant round complexity in the corrupt majority setting—or more precisely, when $$f \ge n/2 + \omega (1)$$ —remains unknown, where f denotes the number of corrupt nodes. In this paper, we are the first to resolve this long-standing question. We show how to achieve BB in expected $$O((n/(n-f))^2)$$ rounds. Our results hold under a weakly adaptive adversary who cannot perform “after-the-fact removal” of messages already sent by a node before it becomes corrupt. We also assume trusted setup and the Decision Linear (DLIN) assumption in bilinear groups.
Eye Movement Biometrics Using a New Dataset Collected in Virtual Reality

This paper introduces a novel eye movement dataset collected in virtual reality (VR) that contains both 2D and 3D eye movement data from over 400 subjects. We establish that this dataset is suitable for biometric studies by evaluating it with both statistical and machine learning–based approaches. For comparison, we also include results from an existing, similarly constructed dataset.
Clinical Application of Image Analysis in Pathology

Quantitative biomarkers are key prognostic and predictive factors in the diagnosis and treatment of cancer. In the clinical laboratory, the majority of biomarker quantitation is still performed manually, but digital image analysis (DIA) methods have been steadily growing and account for around 25% of all quantitative immunohistochemistry (IHC) testing performed today. Quantitative DIA is primarily employed in the analysis of breast cancer IHC biomarkers, including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2/neu; more recently clinical applications have expanded to include human epidermal growth factor receptor 2/neu in gastroesophageal adenocarcinomas and Ki-67 in both breast cancer and gastrointestinal and pancreatic neuroendocrine tumors. Evidence in the literature suggests that DIA has significant benefits over manual quantitation of IHC biomarkers, such as increased objectivity, accuracy, and reproducibility. Despite this fact, a number of barriers to the adoption of DIA in the clinical laboratory persist. These include difficulties in integrating DIA into clinical workflows, lack of standards for integrating DIA software with laboratory information systems and digital pathology systems, costs of implementing DIA, inadequate reimbursement relative to those costs, and other factors. These barriers to adoption may be overcome with international standards such as Digital Imaging and Communications in Medicine (DICOM), increased adoption of routine digital pathology workflows, the application of artificial intelligence to DIA, and the emergence of new clinical applications for DIA.
SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery

In this paper we propose to embed SMPL within a deep-based model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 12 mm, respectively.
Computing Students' Learning Difficulties in HCI Education

Software developers often make interface design decisions and work with designers. Therefore, computing students who seek to become developers need some education about interface design. While prior work has studied difficulties that educators face when teaching design to computing students, there is comparatively little work on the difficulties computing students face when learning HCI design skills. To uncover these difficulties, we conducted two qualitative studies consisting of surveys and interviews with (1) computing students and (2) educators who teach interface design to computing students. Qualitative analysis of their responses revealed 18 types of learning difficulties students might experience in HCI design education, including difficulties around the mechanics of design work, project management skills, the wicked nature of design problems, and distorted perspectives on design.
A critical review on blockchain assessment initiatives: A technology evolution viewpoint

Abstract Blockchain is considered as a major emerging technology that is having an ever‐increasing spread both in industrial and academic contexts. As the usage of blockchain keeps increasing, a fourth generation of blockchain platforms is being proposed. Thus, applications of blockchain have evolved towards wider scopes than cryptocurrency and asset management. In this context, it is important for practitioners to have deep understanding of various blockchain assessment initiatives. Therefore, this work discusses blockchain assessment initiatives from a technology evolution viewpoint. Furthermore, a mapping was conducted to identify factors that impact blockchain initiatives, synthesize available evidence, and identify gaps between relevant approaches available in the literature. As a result, nine selected works were analyzed based on applicability, research approach, assessment process, blockchain adoption process, and blockchain waves. The findings can help practitioners to understand the main assessment factors that undermine blockchain implementations.
Passphrase and keystroke dynamics authentication: Usable security

It was found that employees spend a total of 2.25 days within 60 days on password-related activities. The time consumed by this is unproductive and has a negative impact on usability. The problem is caused by current text-based user authentication policies in use. This study aims to address this research problem by assessing the effectiveness of a proposed two-tier user authentication solution involving passphrases and keystroke dynamics. A design science research approach was used to guide this study, the theoretical foundation of which included three theories: the Shannon Entropy theory which was used to calculate the strength of passwords, passphrases and keystroke dynamics; Chunking theory assisted in assessing password and passphrase memorisation issues; and the Keystroke Level model was used to assess password and passphrase typing issues. Two primary data collection methods were used to evaluate the findings and to ensure that gaps in the research were filled. Firstly, a login assessment experiment was used to collect data on user authentication and user–system interaction for passwords and passphrases and, secondly, an expert review was conducted to validate findings and assess the research artefact in the form of a model. The model was finalised after it had been updated based on the expert review feedback. The model indicates the components that should be considered to implement the user authentication solution successfully. If all the model components are considered, the proposed two-tier user authentication solution has the potential to improve security and usability in the user authentication process.
Shelfie

Self-monitoring devices are becoming increasingly popular in the support of physical activity experiences. These devices mostly represent on-screen data using numbers and graphs and in doing so, they may miss multi-sensorial methods for engaging with data. Embracing the opportunity for pleasurable interactions with one's own data through the use of different materials and digital fabrication technology, we designed and studied three systems that turn this data into 3D-printed plastic artifacts, sports drinks, and 3D-printed chocolate treats. We utilize the insights gained from associated studies, related literature, and our experiences in designing these systems to develop a conceptual framework, “ Shelfie. ” The “ Shelfie ” framework has 13 cards that convey key themes for creating material representations of physical activity data. Through this framework, we present a conceptual understanding of relationships between material representation and physical activity data and contribute guidelines to the design of meaningful material representations of physical activity data.
An unsupervised image segmentation method combining graph clustering and high-level feature representation

Image segmentation is one of the most important assignments in computer vision. In this paper, we present an unsupervised segmentation method that combines graph-based clustering and high-level semantic features. We over-segment the given image into a collection of superpixels. Various low-level features assemble a descriptor of each superpixel. Besides the intrinsic image features such as color, texture and gradient, we add image saliency into the low-level visual features as prior knowledge of human perception. Instead of using the low-level features directly, we design a graph-based method to segment the image by clustering the high-level semantic features learned from a neural network. We test the proposed method on two well-known datasets. The experimental evaluation validates that our approach can provide consistent and meaningful segmentation.
Efficient handling of string-number conversion

String-number conversion is an important class of constraints needed for the symbolic execution of string-manipulating programs. In particular solving string constraints with string-number conversion is necessary for the analysis of scripting languages such as JavaScript and Python, where string-number conversion is a part of the definition of the core semantics of these languages. However, solving this type of constraint is very challenging for the state-of-the-art solvers. We propose in this paper an approach that can efficiently support both string-number conversion and other common types of string constraints. Experimental results show that it significantly outperforms other state-of-the-art tools on benchmarks that involves string-number conversion.
Enhancing apportionment of the point and diffuse sources of soil heavy metals using robust geostatistics and robust spatial receptor model with categorical soil-type data

Soil-type data usually contain valuable information about soil heavy metal (HM) concentrations; however, they were rarely considered in the apportionment of point or diffuse sources in previous studies. In this study, the spatial variations of the soil HM concentrations in Jintan County, China were partitioned into two portions - the soil-type effects and the corresponding residuals, using analysis of variance (ANOVA). Standardized robust kriging error (SRKE) with soil-type data as auxiliary information (SRKE-ST) was proposed to identify the high-value spatial outliers of soil HMs, and the performance of SRKE-ST was compared with that of commonly-used SRKE. Robust absolute principal component scores/robust geographically weighted regression (RAPCS/RGWR) with soil-type data as auxiliary information (RAPCS/RGWR-ST) was proposed to apportion the diffuse sources of soil HMs, and the performance of RAPCS/RGWR-ST was compared with those of RAPCS/RGWR and commonly-used absolute principal component scores/multiple linear regression (APCS/MLR). Results showed that (i) RSKE-ST effectively excluded high-value spatial outliers resulting from the effects of complex soil-type polygons on soil HM concentrations; (ii) RAPCS/RGWR-ST generated higher estimation accuracy in source contributions and less negative contributions than RAPCS/RGWR and APCS/MLR did. It is concluded that the proposed RSKE-ST and RAPCS/RGWR-ST could effectively use categorical soil-type data to enhance, respectively, the identification of high-value spatial outliers (i.e., potential point sources) and the apportionment of diffuse sources of soil HMs in large-scale areas.
Investigating English Speaking Problems: Implications for Speaking Curriculum Development in Indonesia

&lt;p style="text-align: justify;"&gt;The present study aims at investigating some English speaking problems found by the students of English language education department at State Islamic Institute of Kudus and exploring some implications for speaking curriculum development of English language education department. This study used a descriptive qualitative approach and the data were taken from semi-structured interviews, observation and focus group discussion (FGD). The results of the analysis reveal that the problems encountered by the students in English language education department in speaking English cover the lack of appropriate vocabulary, the lack of grammar mastery, the lack of correct pronunciation, the lack of input of English outside the class, the lack of confidence and the lack of English speaking curriculum development. There have been some implications for developing English speaking curriculum. First, the curriculum of speaking should be well designed. Second, lecturers should design English speaking curriculum integrated by technology and social media that makes students to enrich some culture, knowledge and experience around the world. Third, lecturers should facilitate the English speaking curriculum with the English speaking community so that the students will have more chance to speak English inside and outside the class.&lt;/p&gt;
Content Word Aware Neural Machine Translation

Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-byword.However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words).To address this limitation, we first utilize word frequency information to distinguish between content and function words in a sentence, and then design a content word-aware NMT to improve translation performance.Empirical results on the WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chineseto-English translation tasks show that the proposed methods can significantly improve the performance of Transformer-based NMT.