Reviewerly/exHarmony huggingface dataset:
Body LayARs: A Toolkit for Body-Based Augmented Reality

Technological advances are enabling a new class of augmented reality (AR) applications that use bodies as substrates for input and output. In contrast to sensing and augmenting objects, body-based AR applications track people around the user and layer information on them. However, prototyping such applications is complex, time-consuming, and cumbersome, due to a lack of easily accessible tooling and infrastructure. We present Body LayARs, a toolkit for fast development of body-based AR prototypes. Instead of directly programming for a device, Body LayARs provides an extensible graphical programming environment with a device-independent runtime abstraction. We focus on face-based experiences for headset AR, and show how Body LayARs makes a range of body-based AR applications fast and easy to prototype.
Semantic Modeling of Virtual Reality Training Scenarios

Virtual reality can be an effective tool for professional training, especially in the case of complex scenarios, which performed in reality may pose a high risk for the trainee. However, efficient use of VR in practical everyday training requires efficient and easy-to-use methods of designing complex interactive scenarios. In this paper, we propose a new method of creating virtual reality training scenarios, with the use of knowledge representation enabled by semantic web technologies. We have verified the method by implementing and demonstrating an easy-to-use desktop application for designing VR scenarios by domain experts.
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations

Pretrained language models (PLMs) learn stereotypes held by humans and reflected in text from their training corpora, including gender bias. When PLMs are used for downstream tasks such as picking candidates for a job, people’s lives can be negatively affected by these learned stereotypes. Prior work usually identifies a linear gender subspace and removes gender information by eliminating the subspace. Following this line of work, we propose to use DensRay, an analytical method for obtaining interpretable dense subspaces. We show that DensRay performs on-par with prior approaches, but provide arguments that it is more robust and provide indications that it preserves language model performance better. By applying DensRay to attention heads and layers of BERT we show that gender information is spread across all attention heads and most of the layers. Also we show that DensRay can obtain gender bias scores on both token and sentence levels. Finally, we demonstrate that we can remove bias multilingually, e.g., from Chinese, using only English training data.
Machine Sensibility

This paper offers a conceptual contribution to understand 3D printing practice. We have studied conversations between 3D printing practitioners who discuss failed and discarded printed artifacts and analyzed how they make sense of the printing process. Based on findings of interactions with the machine itself, materials used, and designs applied, this study contributes to the field of HCI by highlighting the embodied and situated dimensions of 3D printing. Introducing the concept of machine sensibility, we characterize our findings around: i) assessing printability, ii) monitoring and intervening and iii) reading the prints. We use the term machine to highlight the importance of understanding the materiality of the 3D printer, and sensibility, to address critical interactions and abilities that surfaced in studying this practice. The concept allows researchers to put 3D printing practice in the context of contemporary interaction design research and helps to understand challenges of material-machine-design interdependencies.
Safe Reinforcement Learning via Curriculum Induction

In safety-critical applications autonomous agents may need to learn in an environment where mistakes can be very costly. In such settings, the agent needs to behave safely not only after but also while learning. To achieve this, existing safe reinforcement learning methods make an agent rely on priors that let it avoid dangerous situations during exploration with high probability, but both the probabilistic guarantees and the smoothness assumptions inherent in the priors are not viable in many scenarios of interest such as autonomous driving. This paper presents an alternative approach inspired by human teaching, where an agent learns under the supervision of an automatic instructor that saves the agent from violating constraints during learning. In this model, we introduce the monitor that neither needs to know how to do well at the task the agent is learning nor needs to know how the environment works. Instead, it has a library of reset controllers that it activates when the agent starts behaving dangerously, preventing it from doing damage. Crucially, the choices of which reset controller to apply in which situation affect the speed of agent learning. Based on observing agents’ progress, the teacher itself learns a policy for choosing the reset controllers, a curriculum, to optimize the agent’s final policy reward. Our experiments use this framework in two environments to induce curricula for safe and efficient learning.
CRBHits: From Conditional Reciprocal Best Hits to Codon Alignments and Ka/Ks in R

CRBHits is a coding sequence (CDS) analysis pipeline in R (R Core Team, 2019).It reimplements the Conditional Reciprocal Best Hit (CRBH) algorithm crb-blast and covers all necessary steps from sequence similarity searches, codon alignments to Ka/Ks calculations and synteny.The new R package targets ecology, population and evolutionary biologists working in the field of comparative genomics.
A parametrized ranking-based semantics compatible with persuasion principles

In this work, we question the ability of existing ranking-based semantics to capture persuasion settings, emphasising in particular the phenomena of procatalepsis (the fact that it is often efficient to anticipate the counter-arguments of the audience) and of fading (the fact that long lines of argumentation become ineffective). Some widely accepted principles of ranking-based semantics (like Void Precedence) are incompatible with a faithful treatment of these phenomena, which means that no existing ranking-based semantics can capture these two principles together. This motivates us to introduce a new parametrized ranking-based semantics based on the notion of propagation which extends the existing propagation semantics (In Proceedings of the 6th International Conference on Computational Models of Argument (COMMA’16) (2016) 139–150) by adding an additional parameter allowing us to gradually decrease the impact of arguments when the length of the path between two arguments increases. We show that this parameter gives the possibility of choosing if one wants to satisfy the property Void Precedence or not (and then capture procatalepsis) and to control the scope of the impact of the arguments (and then to capture fading principle). We also propose an experiment to show that the new semantics remains stable when this parameter varies and an axiomatic evaluation to compare it with existing ranking-based semantics in the literature.
Answering Comparative Questions: Better than Ten-Blue-Links?

We present CAM (comparative argumentative machine), a novel open-domain IR system to argumentatively compare objects with respect to information extracted from the Common Crawl. In a user study, the participants obtained 15% more accurate answers using CAM compared to a traditional keyword-based search and were 20% faster in finding the answer to comparative questions.
Alignment-free Cross-lingual Semantic Role Labeling

Cross-lingual semantic role labeling (SRL) aims at leveraging resources in a source language to minimize the effort required to construct annotations or models for a new target language.Recent approaches rely on word alignments, machine translation engines, or preprocessing tools such as parsers or taggers.We propose a cross-lingual SRL model which only requires annotations in a source language and access to raw text in the form of a parallel corpus.The backbone of our model is an LSTM-based semantic role labeler jointly trained with a semantic role compressor and multilingual word embeddings.The compressor collects useful information from the output of the semantic role labeler, filtering noisy and conflicting evidence.It lives in a multilingual embedding space and provides direct supervision for predicting semantic roles in the target language.Results on the Universal Proposition Bank and manually annotated datasets show that our method is highly effective, even against systems utilizing supervised features. 1
What is the minimum CHSH score certifying that a state resembles the singlet?

A quantum state can be characterized from the violation of a Bell inequality. The well-known CHSH inequality for example can be used to quantify the fidelity (up to local isometries) of the measured state with respect to the singlet state. In this work, we look for the minimum CHSH violation leading to a non-trivial fidelity. In particular, we provide a new analytical approach to explore this problem in a device-independent framework, where the fidelity bound holds without assumption about the internal working of devices used in the CHSH test. We give an example which pushes the minimum CHSH threshold from<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≈</mml:mo><mml:mn>2.0014</mml:mn></mml:math>to<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≈</mml:mo><mml:mn>2.05</mml:mn><mml:mo>,</mml:mo></mml:math>far from the local bound. This is in sharp contrast with the device-dependent (two-qubit) case, where entanglement is one-to-one related to a non-trivial singlet fidelity. We discuss this result in a broad context including device-dependent/independent state characterizations with various classical resources.
Regularized Spectral Clustering With Entropy Perturbation

Spectral clustering is a popular clustering method because it gives a natural way to reduce the dimensionality of data using eigenvectors. It is well known that the performance of spectral clustering could be improved via regularization. Nevertheless, it is hard to cope with the different cases by only one constant regularization parameter. To solve such a problem, a novel regularized spectral clustering method is proposed. Specifically, two modules are integrated in the proposed method. First, under matrix perturbation analysis, we prove that the entropy can be used as a rank score function to reveal the informative eigenvector, and the eigenvector corresponding to the minimal entropy will be the regularization to regularize the data matrix instead of a constant regularization parameter. Second, in order to ensure the perturbation on eigenspace is within the effective range, a perturbation boundary on eigenvectors is given. Numerical results showed that our proposal has superior performance than spectral clustering and k-means algorithm.
On the Importance of the DCT Phase for Image Steganography Schemes

There is a well-known theory in the literature that as long as the Fourier phase of an image is maintained intact, the overall appearance of an image remains specious if the Fourier magnitude of the image is slightly modified. However, the Discrete Cosine Transform (DCT) is the real number transformation version of the Fourier transform. Therefore, there is no phase spectrum in the DCT transform. However, it is noticeable that several state-of-the-art DCT hiding schemes treat the DCT signs as phase signals, and hide only in the DCT magnitude spectrum. However, there is no solid study on the importance of the DCT signs (phase) on image steganography schemes. Thus, this paper aims to highlight the impact of the DCT phase on hiding schemes, and to discover the degree of improvement achieved by simply keeping the DCT signs intact while hiding the secret message.
Omission-Based Abstraction for Answer Set Programs

Abstraction is a well-known approach to simplify a complex problem by over-approximating it with a deliberate loss of information. It was not considered so far in Answer Set Programming (ASP), a convenient tool for problem solving. We introduce a method to automatically abstract ASP programs that preserves their structure by reducing the vocabulary while ensuring an over-approximation (i.e., each original answer set maps to some abstract answer set). This allows for generating partial answer set candidates that can help with approximation of reasoning. Computing the abstract answer sets is intuitively easier due to a smaller search space, at the cost of encountering spurious answer sets. Faithful (non-spurious) abstractions may be used to represent projected answer sets and to guide solvers in answer set construction. For dealing with spurious answer sets, we employ an ASP debugging approach to help with abstraction refinement, which determines atoms as badly omitted and adds them back in the abstraction. As a show case, we apply abstraction to explain unsatisfiability of ASP programs in terms of blocker sets, which are the sets of atoms such that abstraction to them preserves unsatisfiability. Their usefulness is demonstrated by experimental results.
Systematically Quantifying IoT Privacy Leakage in Mobile Networks

Privacy leakage of Internet of Things (IoT) has become a great challenge with the popularity of IoT services through mobile networks, such as smart homes, wearables, and healthcare. While previous work summarized general structures to analyze IoT privacy and provide case studies of specific devices or scenarios, it is still challenging to conduct a comprehensive and systematic quantification study of large-scale IoT privacy leakage in real world. To combine systematic analyses with real-world measurements, we provide a method to quantify IoT privacy leakage on a large-scale mobile network traffic data set containing 47651 IoT devices. We generate privacy fingerprints and attribute them to a privacy quantification framework. The framework is constructed based on the semantics of multiple privacy sensitive markers selected from the traffic along with the involved network entity types in IoT (i.e., user, device, and platform), and the fingerprints are generated from sensitive information extracted in the traffic via their markers. Our quantification shows that IoT users, devices, and platforms have considerable risks, respectively. Moreover, IoT devices have a larger scale of privacy leakage than users and platforms, and they perform different daily patterns on privacy leakage following their working conditions. In addition, we present three case studies on the leakage of location information, application calling, and voice service, which illustrate that a third party can profile a network entity in both cyberspace and physical space.
Novelty Search in Representational Space for Sample Efficient Exploration

We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives. Our approach uses intrinsic rewards that are based on the distance of nearest neighbors in the low dimensional representational space to gauge novelty. We then leverage these intrinsic rewards for sample-efficient exploration with planning routines in representational space for hard exploration tasks with sparse rewards. One key element of our approach is the use of information theoretic principles to shape our representations in a way so that our novelty reward goes beyond pixel similarity. We test our approach on a number of maze tasks, as well as a control problem and show that our exploration approach is more sample-efficient compared to strong baselines.
Comparison of Surface Solar Irradiance from Ground Observations and Satellite Data (1990–2016) over a Complex Orography Region (Piedmont—Northwest Italy)

Climate Monitoring Satellite Application Facility (CM SAF) surface solar irradiance (SSI) products were compared with ground-based observations over the Piedmont region (north-western Italy) for the period 1990–2016. These products were SARAH-2.1 (Surface Solar Radiation DataSet—Heliosat version 2.1) and CLARA-A2 (Cloud, Albedo and Surface Radiation dataset version A2). The aim was to contribute to the discussion on the representativeness of satellite SSI data including a focus on high-elevation areas. The comparison between SSI averages shows that for low OCI (orographic complexity index) stations, satellite series have higher values than corresponding ground-based observations, whereas for high OCI stations, SSI values for satellite records are mainly lower than for ground stations. The comparison between SSI anomalies highlights that satellite records have an excellent performance in capturing SSI day-to-day variability of ground-based low OCI stations. In contrast, for high OCI stations, the agreement is much lower, due to the higher uncertainty in both satellite and ground-based records. Finally, if the temporal trends are considered, average low-elevation ground-based SSI observations show a positive trend, whereas satellite records do not highlight significant trends. Focusing on high-elevation stations, the observed trends for ground-based and satellite records are more similar with the only exception of summer. This divergence seems to be due to the relevant role of atmospheric aerosols on SSI trends.
SecureTrain: An Approximation-Free and Computationally Efficient Framework for Privacy-Preserved Neural Network Training

Data privacy is a fundamental challenge for Deep Learning (DL) in many applications. In this work, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecureTrain</i> , which aims to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">carry out privacy-preserved DL model training efficiently and without accuracy loss</i> . SecureTrain enables joint linear and non-linear computation based on the Homomorphic Secret Share (HSS) technique,to carry out approximation-free non-polynomial operations, to achieve training stability and prevent accuracy loss. Meanwhile, it eliminates the time consuming Homomorphic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">permutation operation</i> (Perm) and features an efficient piggyback design,by carefully devising the share set and exploiting the dataflow of the whole training process. This design significantly reduces the overall system training time. We analyze the computation and communication complexity of SecureTrain and prove its security. We implement SecureTrain and benchmark its performance with well-known dataset for both inference and training. For inference, SecureTrain not only ensures privacy-preserved inference, but achieves an inference speedup as high as 48× compared with state-of-the-art inference frameworks. For training, SecureTrain maintains the model accuracy and stability comparable to plaintext training, which is a sharp contrast to other schemes. To the best of knowledge, this is the first work that addresses two fundamental challenges, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accuracy loss/training instability, and computation efficiency</i> , in privacy-preserved deep neural network training.
Emotion Specific Human Face Authentication Based on Infrared Thermal Image

Facial emotion authentication is an emergent topic during the last few decades. It depicts a human's mood and reflects his activity that he is doing, going to do and thinking to do. Activity-mapping is possible to establish by analyzing emotions' sequence from the classification of facial expressions. To identify and recognize emotion the whole face is divided into four classes and into eight regions namely forehead (left, right), eyes (left, right), lips (left, right), and chin (left, right). However, the importance is being given to the region of interest (ROI). Based on the ROI four regions are been chosen (Nose-tip, Left-eye, Right-eye and Lip). Once classification and recognition are completed a database termed as image-data-mask is maintained. The correlation between variances and standard deviations is established based on one identified image. In the process of classification and recognition, and Optimized Probability Density Function (OPDF) is proposed. The centralized database (image-data-mask) is being checked before registration of a new image into the system to avoid redundancy. Nose-tip is taken as the central-point and rest regions are being detected based on it. In this investigation, the emotions (normal, fear, and smiley) are considered and the infrared thermal images are also recorded concurrently. A calibration technique is implemented to establish a matching between vectors of face-ROI and its features. The investigational result illustrates the supremacy of the proposed method as compared to other investigators.
Term position‐based language model for information retrieval

Abstract Term position feature is widely and successfully used in IR and Web search engines, to enhance the retrieval effectiveness. This feature is essentially used for two purposes: to capture query terms proximity or to boost the weight of terms appearing in some parts of a document. In this paper, we are interested in this second category. We propose two novel query‐independent techniques based on absolute term positions in a document, whose goal is to boost the weight of terms appearing in the beginning of a document. The first one considers only the earliest occurrence of a term in a document. The second one takes into account all term positions in a document. We formalize each of these two techniques as a document model based on term position, and then we incorporate it into a basic language model (LM). Two smoothing techniques, Dirichlet and Jelinek‐Mercer, are considered in the basic LM. Experiments conducted on three TREC test collections show that our model, especially the version based on all term positions, achieves significant improvements over the baseline LMs, and it also often performs better than two state‐of‐the‐art baseline models, the chronological term rank model and the Markov random field model.
On the impact of release policies on bug handling activity: A case study of Eclipse

Large software projects follow a continuous development process with regular releases during which bugs are handled. In recent years, many software projects shifted to rapid releases that reduce time-to-market and claim a faster delivery of fixed issues, but also have a shorter period to address bugs. To better understand the impact of rapid releases on bug handling activity, we empirically analyze successive releases of the Eclipse Core projects, focusing on the bug handling rates and durations as well as the feature freeze period. We study the impact of Eclipse’s transition from a yearly to quarterly release cycle. We confirm our findings through feedback received from five Eclipse Core maintainers. Among others, our results reveal that Eclipse’s bug handling process is becoming more stable over time, with a decreasing number of reported bugs before releases, an increasing bug fixing rate and an increasingly balanced bug handling workload before and after releases. The transition to a quarterly release cycle continued to improve bug handling. In addition, more effort is spent on bug fixing during the feature freeze period, while the bug handling rates do not differ between both periods.