Reviewerly/exHarmony huggingface dataset:
Changing Perspective

Conventional hearing aids frame hearing impairment almost exclusively as a problem. In the present paper, we took an alternative approach by focusing on positive future possibilities of 'divergent hearing'. To this end, we developed a method to speculate simultaneously about not-yet-experienced positive meanings and not-yet-existing technology. First, we gathered already existing activities in which divergent hearing was experienced as an advantage rather than as a burden. These activities were then condensed into 'Prompts of Positive Possibilities' (PPP), such as 'Creating a shelter to feel safe in". In performative sessions, participants were given these PPPs and 'Open Probes' to enact novel everyday activities. This led to 26 possible meanings and according devices, such as "Being able to listen back into the past with a rewinder". The paper provides valuable insights into the interests and expectations of people with divergent hearing as well as a methodological contribution to a possibility-driven design.
Robust three-stage unscented Kalman filter for Mars entry phase navigation

A high-precision Mars entry navigation algorithm under parameter uncertainties and unknown inputs is developed via a robust three-stage unscented Kalman filter. The uncertainties lie in the atmosphere density, ballistic coefficient and lift-to-drag ratio are the most important error source of state estimation, which have been investigated in this paper. As the robust three-stage unscented Kalman filter has great advantages in parameter estimation and state estimation for nonlinear system with unknown input, it is proposed to compensate the effect of those parameter uncertainties and unknown inputs and obtain more accurate estimation. In the numerical simulation of Mars entry navigation, this filter algorithm has shown its advantages by resulting a high precision navigation with position error less than 100 m and velocity error less than 2 m/s, which fulfills the requirement of future pinpoint Mars landing missions.
Register in computational language research

Abstract Shlomo Argamon is Professor of Computer Science and Director of the Master of Data Science Program at the Illinois Institute of Technology (USA). In this article, he reflects on the current and potential relationship between register and the field of computational linguistics. He applies his expertise in computational linguistics and machine learning to a variety of problems in natural language processing. These include stylistic variation, forensic linguistics, authorship attribution, and biomedical informatics. He is particularly interested in the linguistic structures used by speakers and writers, including linguistic choices that are influenced by social variables such as age, gender, and register, as well as linguistic choices that are unique or distinctive to the style of individual authors. Argamon has been a pioneer in computational linguistics and NLP research in his efforts to account for and explore register variation. His computational linguistic research on register draws inspiration from Systemic Functional Linguistics, Biber’s multi-dimensional approach to register variation, as well as his own extensive experience accounting for variation within and across text types and authors. Argamon has applied computational methods to text classification and description across registers – including blogs, academic disciplines, and news writing – as well as the interaction between register and other social variables, such as age and gender. His cutting-edge research in these areas is certain to have a lasting impact on the future of computational linguistics and NLP.
Leakage mitigation for quantum error correction using a mixed qubit scheme

Leakage errors take qubits out of the computational subspace and will accumulate if not addressed. A leaked qubit will reduce the effectiveness of quantum error correction protocols due to the cost of implementing leakage reduction circuits and the harm caused by interacting leaked states with qubit states. Ion trap qubits driven by Raman gates have a natural choice between qubits encoded in magnetically insensitive hyperfine states that can leak and qubits encoded in magnetically sensitive Zeeman states of the electron spin that cannot leak. In our previous work, we compared these two qubits in the context of the toric code with a depolarizing leakage error model and found that for magnetic field noise with a standard deviation less than 32 $μ$G that the $^{174}$Yb$^+$ Zeeman qubit outperforms the $^{171}$Yb$^+$ hyperfine qubit. Here we examine a physically motivated leakage error model based on ions interacting via the Molmer-Sorenson gate. We find that this greatly improves the performance of hyperfine qubits but the Zeeman qubits are more effective for magnetic field noise with a standard deviation less than 10 $μ$G. At these low magnetic fields, we find that the best choice is a mixed qubit scheme where the hyperfine qubits are the ancilla and the leakage is handled without the need of an additional leakage reduction circuit.
EOG-based eye tracking protocol using baseline drift removal algorithm for long-term eye movement detection

This paper presents a new method to remove baseline drift and noise by using a differential electrooculography (EOG) signal based on a fixation curve (DOSbFC) and a new electrode positioning scheme based on eyeglasses for user convenience. In addition, a desktop application and mobile applications to control the human–computer interface were implemented. Finally, we created experimental EOG eyeglasses and a new detection protocol using the proposed method for long-term step-by-step detection of eye movements and user comfort. The proposed DOSbFC calculates the difference values of accumulated EOG signals between the initial eye movement and fixation time. It allows long-term detection of eye movements with high accuracy and only requires a single calibration. The vertical and ground electrodes of the standard electrode positioning scheme caused discomfort of subjects; the proposed electrode positioning scheme solves these problems and enables the use of existing eyeglasses without design modification. The experimental results demonstrated that the average accuracy of the long-term eye movement detection was 94%, whereas those of the band pass filter and wavelet transform were 61% and 64%, respectively. This was because baseline drift and noise were removed by averaging the signal variations. Further experimental results demonstrated that the average information transfer rate of the proposed method was 6.0, whereas those of the band pass filter and wavelet transform were 1.1 and 0.9, respectively.
An image watermarking method based on visual saliency and contourlet transform

For the purpose of enhancing the imperceptibility and the robustness of watermark, a new image watermarking method based on visual saliency and contourlet transform is proposed. In this algorithm, salient object detection and contourlet transform are operated separately on host image. After dividing the transformed low-pass sub-band into nonoverlapping blocks, the saliency values and the energy distribution of the block determine the quantization step-size of each block together. The logarithmic quantization index modulation is modified so as to gain the robustness against the amplitude scaling attack. Then, in each low-pass sub-band block, the maximum singular value is chosen to embed watermark bits using the modified logarithmic quantization index modulation (MLQIM). The watermarked image is obtained after carrying out the inverse contourlet transform. The results of experiments demonstrate that the presented algorithm possesses satisfactory imperceptibility and is superior to other common algorithms in terms of the robustness against various attacks.
Understanding Visual Cues in Visualizations Accompanied by Audio Narrations

It is often assumed that visual cues, which highlight specific parts of a visualization to guide the audience's attention, facilitate visualization storytelling and presentation. This assumption has not been systematically studied. We present an in-lab experiment and a Mechanical Turk study to examine the effects of integral and separable visual cues on the recall and comprehension of visualizations that are accompanied by audio narration. Eye-tracking data in the in-lab experiment confirm that cues helped the viewers focus on relevant parts of the visualization faster. We found that in general, visual cues did not have a significant effect on learning outcomes, but for specific cue techniques (e.g. glow) or specific chart types (e.g heatmap), cues significantly improved comprehension. Based on these results, we discuss how presenters might select visual cues depending on the role of the cues and the visualization type.
Mapping the Margins

Work addressing the negative impacts of domestic violence on victim-survivors and service providers has slowly been contributing to the HCI discourse. However, work discussing the necessary, pre-emptive steps for researchers to enter these spaces sensitively and considerately, largely remains opaque. Heavily-politicised specialisms that are imbued with conflicting values and practices, such as domestic violence service delivery can be especially difficult to navigate. In this paper, we report on a mixed methods study consisting of interviews, a design dialogue and an ideation workshop with domestic violence service providers to explore the potential of an online service directory to support their work. Through this three-stage research process, we were able to characterise this unique service delivery landscape and identify tensions in services' access, understandings of technologies and working practices. Drawing from our findings, we discuss opportunities for researchers to work with and sustain complex information ecologies in sensitive settings.
Driver Face Recognition and Sober Drunk Classification using Thermal Images

A major share of accidents happening today is categorised under drunk and drive accidents. Attempts to curb these accidents are limited to manual checking of drivers and awareness programs, which is evidently not enough or stringent. We propose a system where the driver's face is captured in thermal image spectrum and is first recognised using facial recognition, then classified as drunk or sober. The former is done using a deep learning tool that is Convolution Neural Network and the latter is done using Gaussian Mixture Model along with Fischer Linear Discriminant for dimensionality reduction. Post the facial recognition, we will be using capillary junction points on faces to determine difference in blood temperature thus allowing us to classify them as drunk or not.
Semiquantum secure direct communication with authentication based on single-photons

Semiquantum cryptography has drawn much attention in recent years since it simplifies the implementation of quantum cryptographic protocols. In this paper, we study semiquantum secure direct communication (SQSDC). Based on single-photons, we propose a two-step SQSDC protocol with identity authentication, which can be used to prevent impersonation and the man-in-the-middle attack. Different from previous protocols based on quantum entanglement or quantum memory, our protocol removes there restrictions for simplifying its physical implementation. We prove that our two-step SQSDC protocol is completely robust. That is, any adversarial attempting to learn some information on the secure message or identity strings will inevitably induce some errors that can be detected by communicators.
SuperVision

In this work, we challenge the Gaze interaction paradigm "What you see is what you get" to introduce "playing with peripheral vision". We developed the conceptual framework to introduce this novel gaze-aware game dynamic. We illustrated the concept with SuperVision, a collection of three games that play with peripheral vision. We propose perceptual and interaction challenges that require players not to look and rely on their periphery. To validate the game dynamic and experience, we conducted a user study with twenty-four participants. Results show how the game concept created an engaging and playful experience playing with peripheral vision. Participants showed proficiency in overcoming the game challenges, developing clear strategies to succeed. Moreover, we found evidence that playing the game can affect our visual skills, with greater peripheral awareness.
Selective Ensemble Classification of Image Steganalysis Via Deep Q Network

Currently many important advances in digital media field have been achieved through the ensemble method. In image steganalysis, the application of classifiers has evolved from the early single classifier to the ensemble classifiers. The performance of the ensemble classifier is better than that of a single classifier, but the classifiers may have a certain degree of redundancy. Therefore, it is of great significance to study how to reduce the number of the ensemble classifiers under the premise of ensuring the classification performance. In this letter, we propose a selective ensemble method in image steganalysis based on deep Q network, which combines reinforcement learning with convolutional neural network and are seldom seen in ensemble pruning. This method improves the generalization performance of the model, and reduces the size of ensemble as well. The experimental results show that the proposed method has a certain degree of effect on the ensemble classification optimization of image steganalysis in both spatial and frequency domains.
Privacy-Preserving Distributed Data Fusion Based on Attribute Protection

Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or other reasoning attacks, and individual attributes are not leaked. To achieve this, we present three algorithms that satisfy K-anonymous and differential privacy. Experimental results on real datasets suggest that the proposed algorithm can effectively preserve information in data mining tasks.
An Indoor Scene Classification Method for Service Robot Based on CNN Feature

Indoor scene classification plays a vital part in environment cognition of service robot. With the development of deep learning, fine-tuning CNN (Convolutional Neural Network) on target datasets has become a popular way to solve classification problems. However, this method cannot obtain satisfying indoor scene classification results because of overfitting when scene training datasets are insufficient. To solve this problem, an indoor scene classification method is proposed in this paper, which utilizes CNN feature of scene images to generate scene category features to classify scenes by a novel feature matching algorithm. The novel feature matching algorithm can further improve the speed of scene classification. In addition, overfitting is eliminated by our method even though the training data is limited. The presented method was evaluated on two benchmark scene datasets, Scene 15 dataset and MIT 67 dataset, acquiring 96.49% and 81.69% accuracy, respectively. The experiment results showed that our method was superior to other scene classification methods in terms of accuracy, speed, and robustness. To further evaluate our method, test experiments on unknown scene images from SUN 397 dataset had been done, and the models based on different training datasets obtained 94.34% and 79.80% test accuracy severally, which proved that the proposed method owned good performance in indoor scene classification.
Personalized QoS Prediction for Service Recommendation With a Service-Oriented Tensor Model

Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user's neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy.
A Multi-label Multimodal Deep Learning Framework for Imbalanced Data Classification

Social media and Web services have provided a notable number of multimedia content. Due to such explosion of multimedia data, the multimedia community has been facing new challenges and exciting opportunities these days. This paper presents a new multimedia framework to address some of the main challenges in this area. In particular, it presents a multi-label multimodal framework for imbalanced data classification. For this purpose, it utilizes audio, visual, and textual data modalities and automatically generates static and temporal features using spatio-temporal deep neural networks. It also manages data with non-uniform distributions using a weighted multi-label classifier. To evaluate this framework, a video dataset containing natural disasters is used for multi-label classification. The supremacy of the proposed framework compared to the existing work is revealed with extensive experiments on this dataset.
Design and Implementation of the Smart Glove to Aid the Visually Impaired

Locating objects of daily use is a strenuous task for the visually impaired. The objective of this paper is to design a smart glove by using Deep Neural Networks (DNN) and object tracking algorithm which will guide the hand of the visually impaired to the desired object in an indoor environment. The smart glove has five micro-vibrating motors, each one used to guide the user’s hand in five different directions namely, forward, upward, downward, rightward and leftward. The palm of the glove has a Universal Serial Bus (USB) camera which feeds the real-time video to the Raspberry Pi for processing. The camera also has an inbuilt microphone. The user vocally commands the system to identify the desired object. The camera then detects the object using DNN. Once the object is tagged, object tracking begins. Based on the relative position of the camera and the object, the micro-vibrating motors vibrate accordingly to guide the users hand in the required direction. An additional feature which is incorporated includes differentiation of similar objects by color.
Online Knowledge Level Tracking with Data-Driven Student Models and Collaborative Filtering

Intelligent Tutoring Systems are promising tools for delivering optimal and personalized learning experiences to students. A key component for their personalization is the student model, which infers the knowledge level of the students to balance the difficulty of the exercises. While important advances have been achieved, several challenges remain. In particular, the models should be able to track in real-time the evolution of the students' knowledge levels. These evolutions are likely to follow different profiles for each student, while measuring the exact knowledge level remains difficult given the limited and noisy information provided by the interactions. This paper introduces a novel model that addresses these challenges with three contributions:. 1) the model relies on Gaussian Processes to track online the evolution of the student's knowledge level over time, 2) it uses collaborative filtering to rapidly provide long-term predictions by leveraging the information from previous users, and 3) it automatically generates abstract representations of knowledge components via automatic relevance determination of covariance matrices. The model is evaluated on three datasets, including real users. The results demonstrate that the model converges to accurate predictions in average four times faster than the compared methods.
Initial Coin Offerings: When Are Tokens Securities in the EU and US?

This article presents a comparative analysis of the application of US and EU securities laws to initial coin offerings (ICOs), or token sales. An extensive token taxonomy framework is proposed to categorize digital assets in order to advance a more precise discussion on the legal classification and regulation of tokens. For the US, a full analysis of the application of the Howey test to different types of tokens gives insight into classification of tokens as a security (“investment contract”) under Section 2(a)(1) of the US Securities Act of 1933 and 3(a)(10) of the Securities Exchange Act of 1934. The analysis shows that most, if not all, so-called utility tokens can be classified as a security in the US. A possible ‘sufficiency-of-decentralization-test’ is also explored, while taking prior case law and the multiple dimensions of decentralization of blockchain projects into account. For EU financial law, the analysis in this paper focuses on the classification of tokens as ‘transferable securities’ under Art. 4(1)(44) of MIFiD II. The analysis shows that, in contrast to the US, pure utility tokens might not be deemed transferable securities under the EU securities regime. Across EU Member States however, large differences exist in terms of the legal classification of most tokens, which result from the freedom provided to EU Member States in transposing the MIFiD II definition of transferable securities into national law. The analysis in this paper provides insight into the two main approaches adhered to by EU Member States in the implementation of this definition, as well as their consequences for the legal classification of tokens. Conclusions are subsequently drawn on possible offering strategies adopted by issuers for future token sales and regulatory developments in the US and EU.
SCADFA: Combined SCA+DFA Attacks on Block Ciphers with Practical Validations

We present the first practically realizable side-channel assisted fault attack on any block-ciphers having bit-permutation with optimal diffusion, that can retrieve the round key efficiently using random nibble faults. The attack demonstrates how side-channel leakage can allow the adversary to precisely determine the fault mask resulting from a nibble fault injection instance. We first demonstrate the viability of such attack model via side-channel analysis experiments on top of a laser-based fault injection setup, targeting a PRESENT-80 and GIFT-128 (two popular block-ciphers based on bit-permutation having optimal diffusion) implementation on an ATmega328P microcontroller. Subsequently, we present a differential fault analysis (DFA) exploiting the knowledge of the output fault mask in the target round to recover multiple last round keys nibbles independently and in parallel. We show that the combined attack can recover the last round key of PRESENT-80 and GIFT-128 with 4 random nibble fault injections in the best case. In the average case, the number of random nibble faults required for PRESENT-80 and GIFT-128 are 9-18 and 6-9 respectively.