Reviewerly/exHarmony huggingface dataset:
Quantitative comparison of a mobile and a stationary video-based eye-tracker

Vision represents the most important sense of primates. To understand visual processing, various different methods are employed—for example, electrophysiology, psychophysics, or eye-tracking. For the latter method, researchers have recently begun to step outside the artificial environments of laboratory setups toward the more natural conditions we usually face in the real world. To get a better understanding of the advantages and limitations of modern mobile eye-trackers, we quantitatively compared one of the most advanced mobile eye-trackers available, the EyeSeeCam, with a commonly used laboratory eye-tracker, the EyeLink II, serving as a gold standard. We aimed to investigate whether or not fully mobile eye-trackers are capable of providing data that would be adequate for direct comparisons with data recorded by stationary eye-trackers. Therefore, we recorded three different, commonly used eye movements—fixations, saccades, and smooth-pursuit eye movements—with both eye-trackers, in successive standardized paradigms in a laboratory setting with eight human subjects. Despite major technical differences between the devices, most eye movement parameters were not statistically different between the two systems. Differences could only be found in overall gaze accuracy and for time-critical parameters such as saccade duration, for which a higher sample frequency is especially useful. Although the stationary EyeLink II system proved to be superior, especially on a single-subject or even a single-trial basis, the ESC showed similar performance for the averaged parameters across both trials and subjects. We concluded that modern mobile eye-trackers are well-suited to providing reliable oculomotor data at the required spatial and temporal resolutions.
Evolvability ES

Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge in evolutionary computation; such evolvability is important in practice, because it accelerates evolution and enables fast adaptation to changing circumstances. This paper introduces evolvability ES, an evolutionary algorithm designed to explicitly and efficiently optimize for evolvability, i.e. the ability to further adapt. The insight is that it is possible to derive a novel objective in the spirit of natural evolution strategies that maximizes the diversity of behaviors exhibited when an individual is subject to random mutations, and that efficiently scales with computation. Experiments in 2-D and 3-D locomotion tasks highlight the potential of evolvability ES to generate solutions with tens of thousands of parameters that can quickly be adapted to solve different tasks and that can productively seed further evolution. We further highlight a connection between evolvability in EC and a recent and popular gradient-based meta-learning algorithm called MAML; results show that evolvability ES can perform competitively with MAML and that it discovers solutions with distinct properties. The conclusion is that evolvability ES opens up novel research directions for studying and exploiting the potential of evolvable representations for deep neural networks.
Learning to Match Clothing From Textual Feature-Based Compatible Relationships

This paper presents a new framework for matching clothes by considering item in-between compatibility. In contrast to the use of visual features of clothing items, we only utilized their textual descriptions, i.e., title sentences, to constitute the basic features. Specifically, a longshort-term memory (LSTM) network was used for feature embeddings of title sentences. Given item pairs of queries and candidates, their feature embeddings achieved by Siamese LSTMs were integrated into style-compatible space characterized by a compatibility matrix. Our framework is examined on three large-scaled clothing item sets collected from Amazon, Taobao, and Polyvore, respectively. Experiments confirm the efficacy of our approach compared with several baseline methods.
An Analysis of the Change in Discussions on Social Media with Bitcoin Price

We develop a new approach to temporalizing word2vec-based topic modelling that determines which topics on social media vary with shifts in the phases of a time series to understand potential interactions. This is particularly relevant for the highly volatile bitcoin price with its distinct four phases across 2017-18. We statistically test which words change in frequency between the different stages and compare four word2vec models to assess their consistency in relating connected words in weighted, undirected graphs. For words that fall in frequency when prices shift from rising to falling, all eight topics are identified with the four approaches; for words rising in frequency, three out of the five topics remain constant. These topics are intuitive and match with actual events in the news.
Dual Exclusive Attentive Transfer for Unsupervised Deep Convolutional Domain Adaptation in Speech Emotion Recognition

Considering different corpora of speech emotions available both publicly and privately with numerous factors that make them different, the premise of having features of both training and testing samples drawn from the same distribution and the parameterization of the same feature space is not applicable in most real world scenarios. Addressing this challenge via a domain adaptation method, we propose a dual exclusive attentive transfer (DEAT) for deep convolutional neural network architecture based on unsupervised domain adaptation setting. The proposed architecture adapts to an unshared attentive transfer procedure for convolutional adaptation of both source and target domain. Correlation alignment loss (CALLoss) is applied to minimize the domain shift through the alignment of the second-order statistics of the convolutional layer's attention maps in both domains. Then, for the proposed network to effectively model the shift dissimilar domains, we make the weights of the corresponding layers exclusive but related. The proposed model minimizes the classification loss of the source domain with labels and the correlation alignment loss of both convolutional and fully-connected layers collectively. We evaluate our architecture using the Interspeech 2009 Emotion Challenge FAU Aibo Emotion Corpus as target dataset and two publicly available corpora (ABC and Emo-DB) as source dataset. Our experimental results show that our domain adaptation method is superior to other state-of-the-art methods.
Novel trajectory data publishing method under differential privacy

The existing location-based services have collected a large amount of user trajectory data, and if these data are directly released without any processing, the user's personal privacy will be leaked. At present, differential privacy protection technology is favored by many scholars, but how to apply it reasonably to location-based services is also a challenge for us. Trajectory is spatiotemporal continuous, but most existing methods only consider the single location of moving objects at a certain time without considering the entire trajectory, which may destroy the spatiotemporal integrity of the trajectory. In this paper, we address this problem and firstly propose a Sequence R (SR)-tree structure that satisfies the differential privacy based on the R-tree, and we construct the SR-Tree by using the trajectory sequence instead of the minimum bounding rectangle of the R-tree. Then we put forward an attack model called non-location sensitive information attack, in order to resist this attack, we add noise into the location data and non-location sensitive data using differential privacy techniques. Finally, the Algorithm can be consistently dealt with the problem of data inconsistency after adding noise. Experimental results show that our Algorithm not only has high data availability, operational efficiency, but also has good scalability.
Principal curvatures based local binary pattern for rotation invariant texture classification

Local binary patterns (LBP) have been proved to be an uncomplicated and high-efficiency descriptor for texture classification. However, the LBP and many variants are usually constantness only to restricted rotation angles. To address this limitation, we propose a new local texture descriptor, the principal curvatures based LBP (PCLBP). The proposed PCLBP descriptor is constructed by employing the complementary information of LBP and principal curvatures (PCs) of texture surface. The PCs, which consist of minimum and maximum curvatures, can capture the macro- and micro-structure texture information and have the capability of consecutive rotation invariance. To enhance the discriminativeness and the robustness of the PCLBP, a multiresolution scheme is developed by employing the strategy of cross-scale joint coding as well as adjusting three parameters accordantly. The proposed PCLBP method is simple, training-free, highly discriminative, invariant to illumination and rotation with advantage of computational simplicity. Experimental results on two well-known databases demonstrate that our PCLBP scheme achieves comparable or better performance than a large number of existing descriptors.
Combined problem triggers in simultaneous interpreting: exploring the effect of delivery rate on processing and rendering numbers

The complexity of simultaneous interpreting may be influenced by so-called problem triggers, i.e. factors that increase the interpreter's processing capacity requirements. These items include numbers, which have been observed by many to affect interpreting. The difficulty of interpreting numerals can increase even more when the source text delivery rate is high. The eye-tracking study presented here investigates the effect of the speaker's delivery rate on number interpreting accuracy and eye behaviour. To this end, we tested professional interpreters (N = 30) and interpreting trainees (N = 24). Our results indicate that a high rate of the source text delivery compromises number interpreting accuracy. In addition, fixation count per minute increases when interpreters interpret at a fast pace. Professional interpreters provide more accurate interpretations, irrespective of the pace of speech, indicating the possible necessity of training interpreting students in dealing with problem triggers, such as numbers.
Adversarial Training for Review-Based Recommendations

Recent studies have shown that incorporating users' reviews into the collaborative filtering strategy can significantly boost the recommendation accuracy. A pressing challenge resides on learning how reviews influence users' rating behaviors. In this paper, we propose an Adversarial Training approach for Review-based recommendations, namely ATR. We design a neural architecture of sequence-to-sequence learning to calculate the deep representations of users' reviews on items following an adversarial training strategy. At the same time we jointly learn to factorize the rating matrix, by regularizing the deep representations of reviews with the user and item latent features. In doing so, our model captures the non-linear associations among reviews and ratings while producing a review for each user-item pair. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed model, outperforming other state-of-the-art methods.
bring2lite: A Structural Concept and Tool for Forensic Data Analysis and Recovery of Deleted SQLite Records

As of today mobile applications such as WhatsApp or Skype make use of the SQLite database format to store its data. From an IT forensics point of view it is therefore essential to extract all information related to an SQLite database. In this paper we focus on digital traces in the scope of SQLite that belong to deleted database data. We suggest to make use of a structural approach: we analyse the deletion behaviour of SQLite depending on different database parameters, which affect the erasure of database data. As SQLite pragmas are an important parameter during deletion, we examine the erasure behaviour dependent on the pragmas in use. Based on the results of our analysis, we develop a concept to parse and process deleted records. Our concept is implemented within our tool bring2lite, which is evaluated with respect to a common dataset of SQLite files. The overall result is that bring2lite achieves the highest recovery rate of approximately 53% compared to eight competing programmes.
Vehicle Classification Based on Feature Selection With Anisotropic Magnetoresistive Sensor

Vehicle classification based on magnetic sensors can be effectively applied to intelligent transportation and realize intelligent management of traffic. The use of representative vehicle signal features is a prerequisite and guarantee for accurate vehicle classification. This paper uses a single 3-axis magnetic sensor to acquire vehicle signals and extract a large number of features from the vehicle signals. In order to obtain a set of features that are simple and without loss of classification accuracy, we propose a Filtering algorithm based on Feature Pairing Elimination (FPE-Filter). In addition, choosing the right classifier is also crucial for models with high classification accuracy. In this paper, we compare four common classification models: SVM, RF, KNN, and C4.5. The experimental results show that the SVM performs best and the classification accuracy reaches 95%.
Experimental Self-Characterization of Quantum Measurements

The accurate and reliable description of measurement devices is a central problem in both observing uniquely non-classical behaviors and realizing quantum technologies from powerful computing to precision metrology. To date quantum tomography is the prevalent tool to characterize quantum detectors. However, such a characterization relies on accurately characterized probe states, rendering reliability of the characterization lost in circular argument. Here we report a self-characterization method of quantum measurements based on reconstructing the response range, the entirety of attainable measurement outcomes, eliminating the reliance on known states. We characterize two representative measurements implemented with photonic setups and obtain fidelities above 99.99% with the conventional tomographic reconstructions. This initiates range-based techniques in characterizing quantum systems and foreshadows novel device-independent protocols of quantum information applications.
Interactive and automatic navigation for 360° video playback

A common way to view a 360° video on a 2D display is to crop and render a part of the video as a normal field-of-view (NFoV) video. While users can enjoy natural-looking NFoV videos using this approach, they need to constantly make manual adjustment of the viewing direction not to miss interesting events in the video. In this paper, we propose an interactive and automatic navigation system for comfortable 360° video playback. Our system finds a virtual camera path that shows the most salient areas through the video, generates a NFoV video based on the path, and plays it in an online manner. A user can interactively change the viewing direction while watching a video, and the system instantly updates the path reflecting the intention of the user. To enable online processing, we design our system consisting of an offline pre-processing step, and an online 360° video navigation step. The pre-processing step computes optical flow and saliency scores for an input video. Based on these, the online video navigation step computes an optimal camera path reflecting user interaction, and plays a NFoV video in an online manner. For improved user experience, we also introduce optical flow-based camera path planning, saliency-aware path update, and adaptive control of the temporal window size. Our experimental results including user studies show that our system provides more pleasant experience of watching 360° videos than existing approaches.
A novel variable selection algorithm for multi-layer perceptron with elastic net

The paper proposed a new iterative variable selection algorithm for multi-layer perceptron (MLP) to predict significant variables in complex industrial processes. The proposed algorithm starts from training an ordinary MLP with the original dataset. Following that, the elastic net (EN) algorithm is applied to perform the input weights shrinkage for the well-trained MLP, in which the optimal shrinkage parameters are calculated by grid search and cross-validation. On this basis, a local search strategy is combined to further optimize the input variable selection. The irrelative input variables will be deleted and a new dataset is constructed with remaining variables. This process is repeated until the prescribed termination rules are met. Benchmark datasets with different size and correlation and a practical air separation process are applied and comparisons with other algorithms are made to illustrate its performance. Statistical results show that the proposed algorithm can build more accurate model with fewer variables than other algorithms.
A hybridized methodology of different wavelet transformations targeting medical images in IoT infrastructure

The Internet of Things (IoT) paradigm has become a vital part of all significant scientific sectors, including the healthcare domain. Medical images in the healthcare sector are indispensable items that are usually susceptible to distortion once they are shared and transferred via the Internet. The sector faces the distinct and constant challenge of preserving medical data, which can be manipulated by various malicious attacks, in turn potentially compromising the patients' diagnostic data. In this situation, such medical data ought to be private, with access only granted to patients and physicians. This paper elaborates on a hybrid measurement technique for digital image watermarking that utilizes medical images (X-ray, MRA, and CT), which are an extremely robust method for protecting clinical information. The authors explore various different wavelet families, in addition to hybridization between these wavelets. These are carried out on three levels decomposition of Discrete wavelet transformation (biorthogonal 6.8 wavelets, biorthogonal 3.5 wavelets, biorthogonal 5.5 wavelets, reverse biorthogonal 6.8, reverse biorthogonal 3.5, reverse biorthogonal 5.5, discrete meyer, symlets 5, symlets 8 coiflets 4 wavelet, and coiflets 5 wavelet transform). Each level uses various types of wavelet transformation to present the watermarked image, and then extracts the medical watermark from the original watermarked image. The results of diverse types of attack have been compared, while the proposed measurement technique's performance is evaluated using statistical parameters (MSE, PSNR, SSIM, and NC). This in turn measures the quality of the image, which so far shows promising results.
Fail videos and related video comments on YouTube: a case of sexualization of women and gendered hate speech?

Fail videos showing mishaps/accidents are very popular on YouTube. But is this genre affected by sexism, that is, are women portrayed more often than men in an objectifying, sexualized manner in the video clips (H1), and are women more likely than men to be the target of gendered online hate speech in the video comments (H2)? Quantitative content analyses of 500 video clips (derived from 50 videos) and of 1,000 video comments (derived from 5 "male" and 5 "female" videos) from YouTube's most popular fail video channel FailArmy were conducted. Women in fail videos were portrayed in an objectifying, sexualized manner twice as often (H1), and were the target of gendered hate comments nearly five times more often (H2) compared to men. Future research could analyze videos and comments from additional fail channels and investigate the reasons for the sexualized portrayals as well as for the audience's hateful reactions.
Language choice and gender in a Nordic social media corpus

This study analyzes language choice, bi- and multilingualism, and gender in a corpus of over 22 million Twitter messages by almost 36,000 authors from the Nordic countries and territories. Author location, gender, and tweet language are identified using a novel method. Three principal findings are discussed: First, gendered preference for particular languages in the Nordics can be explained in part by patterns of gendered migration. Second, a distinct geographical pattern of female/male preference for the national languages of the region and for English is evident for users who are likely native users of a Nordic language: Females are more likely to use English, while males are more likely to use a Nordic language. Third, while high rates of bi- and multilingualism are found across the whole sample, males are more likely to use more than one language in all the Nordic countries/territories. The latter two findings are interpreted in light of sociolinguistic considerations as evidence for incipient language shift towards English for Nordic users on the Twitter platform.
Intelligent Autonomous Elderly Patient Home Monitoring System

This paper presents the implementation of an intelligent home-based elderly patient monitoring system. Four patient's physiological parameters are being continuously monitored, namely, temperature, glucose, and 3D accelerometer and gyroscope data for fall detection. Contextual sensors are mounted across the home to observe the patient's surrounding environment such as temperature and humidity. All sensors, wearable and contextual, transmit their measured data to smart gateways (fog layer) via nRF communication protocol. At the fog layer, diverse functions are being carried out, from collected measurements transfer to health care providers for further processing and analysis via Internet (cloud layer), sending push notifications and reports to patient's mobile phone, to alerting ambulance or civil defence authorities in case of an emergency. To insure power autonomy and eliminate the need for frequent sensor node battery replacement, an efficient thermal energy harvesting system is developed. Associated with a boost converter, the thermal energy harvesting system is able to sustain 3.3 OCV leveraging a temperature difference of 20°C between patient's body and room temperature, while achieving an efficiency of 82.6%.
How to Achieve Explainability and Transparency in Human AI Interaction

It is typically not transparent to end-users, how AI systems derive information or make decisions. This becomes crucial, the more pervasive AI systems enter human daily lives, the more they influence automated decision-making, and the more people rely on them. We present work in progress on explainability to support transparency in human AI interaction. In this paper, we discuss methods and research findings on categorizations of user types, system scope and limits, situational context, and changes over time. Based on these different dimensions and their range and combinations, we aim at individual facets of transparency that address a specific situation best. The approach is human-centered to provide adequate explanations with regard to their depth of detail and level of information, and we outline the different dimensions of this complex task.