Reviewerly/exHarmony huggingface dataset:
Identification of Developmental Delay in Infants Using Wearable Sensors: Full-Day Leg Movement Statistical Feature Analysis

This paper examines how features extracted from full-day data recorded by wearable sensors are able to differentiate between infants with typical development and those with or at risk for developmental delays. Wearable sensors were used to collect full-day (8-13 h) leg movement data from infants with typical development ([Formula: see text]) and infants at risk for developmental delay ([Formula: see text]). At 24 months, at-risk infants were assessed as having good ([Formula: see text]) or poor ([Formula: see text]) developmental outcomes. With this limited size dataset, our statistical analysis indicated that accelerometer features collected earlier in infancy differentiated between at-risk infants with poor and good outcomes at 24 months, as well as infants with typical development. This paper also tested how these features performed on a subset of the data for which the infant movement was known, i.e., 5-min intervals more representative of clinical observations. Our results on this limited dataset indicated that features for full-day data showed more group differences than similar features for the 5-min intervals, supporting the usefulness of full-day movement monitoring.
On the Top Threats to Cyber Systems

The technological innovation of cyber systems and increase dependence of individuals, societies and nations on them has brought new, real and everchanging threat landscapes. In fact, the threats evolving faster than they can be assessed. The technological innovation that brought ease and efficiency to our lives, has been met by similar innovation to take advantage of cyber systems for other gains. More threat actors are noted to be sponsored by nation-states and the skills and capabilities of organizations to defend against these attacks are lagging. This warrants an increase in automation of threat analysis and response as well as increased adoption of security measures by at-risk organizations. Thus, to properly prepare defenses and mitigations to the threats introduced by cyber, it is necessary to understand these threats. Accordingly, this paper provides an overview of top cyber security threats in together with current and emerging trends. The analyses include general trends in the complexity of attacks, actors, and the maturity of skills and capabilities of organizations to defend against attacks. Top threats are discussed with regard to instances of attacks and strategies for mitigation within the kill chain. A brief discussion of threat agents and attack vectors adds context to the threats.
Effects of prior knowledge on active vision and memory in younger and older adults.

In our daily lives we rely on prior knowledge to make predictions about the world around us such as where to search for and locate common objects. Yet, equally important in visual search is the ability to inhibit such processes when those predictions fail. Mounting evidence suggests that relative to younger adults, older adults have difficulty retrieving episodic memories and inhibiting prior knowledge, even when that knowledge is detrimental to the task at hand. However, the consequences of these age-related changes for visual search remain unclear. In the present study, we used eye movement monitoring to investigate whether overreliance on prior knowledge alters the gaze patterns and performance of older adults during visual search. Younger and older adults searched for target objects in congruent or incongruent locations in real-world scenes. As predicted, targets in congruent locations were detected faster than targets in incongruent locations, and this effect was enhanced in older adults. Analysis of viewing behavior revealed that prior knowledge effects emerged early in search, as evidenced by initial saccades, and continued throughout search, with greater viewing of congruent regions by older relative to younger adults, suggesting that schema biasing of online processing increases with age. Finally, both younger and older adults showed enhanced memory for the location of congruent targets and the identity of incongruent targets, with schema-guided viewing during search predicting poor memory for schema-incongruent targets in younger adults on both tasks. Our results provide novel evidence that older adults' overreliance on prior knowledge has consequences for both active vision and memory. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Stable Opponent Shaping in Differentiable Games

A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity to multi-agent RL. Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on others' updates. Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner's Dilemma. Although experimentally successful, we show that LOLA agents can exhibit 'arrogant' behaviour directly at odds with convergence. In fact, remarkably few algorithms have theoretical guarantees applying across all (n-player, non-convex) games. In this paper we present Stable Opponent Shaping (SOS), a new method that interpolates between LOLA and a stable variant named LookAhead. We prove that LookAhead converges locally to equilibria and avoids strict saddles in all differentiable games. SOS inherits these essential guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.
Complex Network based Supervised Keyword Extractor

In this paper, we present a supervised framework for automatic keyword extraction from single document. We model the text as complex network, and construct the feature set by extracting select node properties from it. Several node properties have been exploited by unsupervised, graph-based keyword extraction methods to discriminate keywords from non-keywords. We exploit the complex interplay of node properties to design a supervised keyword extraction method. The training set is created from the feature set by assigning a label to each candidate keyword depending on whether the candidate is listed as a gold-standard keyword or not. Since the number of keywords in a document is much less than non-keywords, the curated training set is naturally imbalanced. We train a binary classifier to predict keywords after balancing the training set. The model is trained using two public datasets from scientific domain and tested using three unseen scientific corpora and one news corpus. Comparative study of the results with several recent keyword and keyphrase extraction methods establishes that the proposed method performs better in most cases. This substantiates our claim that graph-theoretic properties of words are effective discriminators between keywords and non-keywords. We support our argument by showing that the improved performance of the proposed method is statistically significant for all datasets. We also evaluate the effectiveness of the pre-trained model on Hindi and Assamese language documents. We observe that the model performs equally well for the cross-language text even though it was trained only on English language documents. This shows that the proposed method is independent of the domain, collection, and language of the training corpora.
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks

Lipschitz constraints under L2 norm on deep neural networks are useful for provable adversarial robustness bounds, stable training, and Wasserstein distance estimation. While heuristic approaches such as the gradient penalty have seen much practical success, it is challenging to achieve similar practical performance while provably enforcing a Lipschitz constraint. In principle, one can design Lipschitz constrained architectures using the composition property of Lipschitz functions, but Anil et al. recently identified a key obstacle to this approach: gradient norm attenuation. They showed how to circumvent this problem in the case of fully connected networks by designing each layer to be gradient norm preserving. We extend their approach to train scalable, expressive, provably Lipschitz convolutional networks. In particular, we present the Block Convolution Orthogonal Parameterization (BCOP), an expressive parameterization of orthogonal convolution operations. We show that even though the space of orthogonal convolutions is disconnected, the largest connected component of BCOP with 2n channels can represent arbitrary BCOP convolutions over n channels. Our BCOP parameterization allows us to train large convolutional networks with provable Lipschitz bounds. Empirically, we find that it is competitive with existing approaches to provable adversarial robustness and Wasserstein distance estimation.
Effects of Age-Related Cognitive Decline on Elderly User Interactions with Voice-Based Dialogue Systems

Cognitive functioning that affects user behaviors is an important factor to consider when designing interactive systems for the elderly, including emerging voice-based dialogue systems such as smart speakers and voice assistants. Previous studies have investigated the interaction behaviors of dementia patients with voice-based dialogue systems, but the extent to which age-related cognitive decline in the non-demented elderly influences the user experiences of modern voice-based dialogue systems remains uninvestigated. In this work, we conducted an empirical study in which 40 healthy elderly participants performed tasks on a voice-based dialogue system. Analysis showed that cognitive scores assessed by neuropsychological tests were significantly related to vocal characteristics, such as pauses and hesitations, as well as to behavioral differences in error-handing situations, such as when the system failed to recognize the user’s intent. On the basis of the results, we discuss design implications towards the tailored design of voice-based dialogue systems for ordinary older adults with age-related cognitive decline.
Constant-Time Machine Translation with Conditional Masked Language Models.

Most machine translation systems generate text autoregressively, bysequentially predicting tokens from left to right. We, instead, use a maskedlanguage modeling objective to train a model to predict any subset of thetarget words, conditioned on both the input text and a partially masked targettranslation. This approach allows for efficient iterative decoding, where wefirst predict all of the target words non-autoregressively, and then repeatedlymask out and regenerate the subset of words that the model is least confidentabout. By applying this strategy for a constant number of iterations, our modelimproves state-of-the-art performance levels for constant-time translationmodels by over 3 BLEU on average. It is also able to reach 92-95% of theperformance of a typical left-to-right transformer model, while decodingsignificantly faster.
Low-Light Image Enhancement Based on Maximal Diffusion Values

A vast amount of pictures are taken every day by using cameras mounted on various mobile devices. Even though the clarity of such acquired images has been significantly improved due to the advance of the image sensor technology, the visual quality is hardly guaranteed under varying illumination conditions. In this paper, a novel yet simple method for low-light image enhancement is proposed via the maximal diffusion value. The key idea of the proposed method is to estimate the illumination component, which is likely to appear as the bright pixel even under the low-light condition, by exploring multiple diffusion spaces. Specifically, the illumination component can be accurately separated from the scene reflectance by selecting the maximal value at each pixel position of those diffusion spaces, and thus independently adjusted for the visual quality enhancement. That is, we propose to adopt the maximal value among diffused intensities at each pixel position, so-called maximal diffusion value, as the illumination component since illumination components buried in the dark tend to be revealed with bright intensities through the iterative diffusion process. In contrast to previous approaches that still pose difficulties to balance between over-saturated and conservative restorations, the proposed method improves the image quality without any significant distortion while successfully suppressing the problem of noise amplification. Experimental results on benchmark datasets show the efficiency and robustness of the proposed method compared to previous approaches introduced in literature.
Provable Filter Pruning for Efficient Neural Networks

We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network. Our algorithm uses a small batch of input data points to assign a saliency score for each filter and constructs an importance sampling distribution where filters that highly affect the output are sampled with correspondingly high probability. Unlike weight pruning approaches that lead to irregular sparsity patterns -- requiring specialized libraries or hardware to enable computational speedups -- our approach compresses the original network to a slimmer subnetwork, which enables accelerated inference with any off-the-shelf deep learning library and hardware. Existing filter pruning methods are generally data-oblivious, rely on heuristics for evaluating the parameter importance, or require manual and tedious hyper-parameter tuning. In contrast, our method is data-informed, exhibits provable guarantees on the size and performance of the pruned network, and is widely applicable to varying network architectures and data sets. Our analytical bounds bridge the notions of compressibility and importance of network structures, which gives rise to a fully-automated procedure for identifying and preserving the filters in layers that are essential to the network's performance. Our experimental results across varying pruning scenarios show that our algorithm consistently generates sparser and more efficient models than those generated by existing filter pruning approaches.
Representation learning for clinical time series prediction tasks in electronic health records

Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, noise, random error and systematic bias. In particular, temporal information is difficult to effectively use by traditional machine learning methods while the sequential information of EHRs is very useful.In this paper, we propose a general-purpose patient representation learning approach to summarize sequential EHRs. Specifically, a recurrent neural network based denoising autoencoder (RNN-DAE) is employed to encode inhospital records of each patient into a low dimensional dense vector.Based on EHR data collected from Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, we experimentally evaluate our proposed RNN-DAE method on both mortality prediction task and comorbidity prediction task. Extensive experimental results show that our proposed RNN-DAE method outperforms existing methods. In addition, we apply the "Deep Feature" represented by our proposed RNN-DAE method to track similar patients with t-SNE, which also achieves some interesting observations.We propose an effective unsupervised RNN-DAE method to summarize patient sequential information in EHR data. Our proposed RNN-DAE method is useful on both mortality prediction task and comorbidity prediction task.
Towards Scale-Free Rain Streak Removal via Self-Supervised Fractal Band Learning

Data-driven rain streak removal methods, which most of rely on synthesized paired data, usually come across the generalization problem when being applied in real cases. In this paper, we propose a novel deep-learning based rain streak removal method injected with self-supervision to improve the ability to remove rain streaks in various scales. To realize this goal, we made efforts in two aspects. First, considering that rain streak removal is highly correlated with texture characteristics, we create a fractal band learning (FBL) network based on frequency band recovery. It integrates commonly seen band feature operations with neural modules and effectively improves the capacity to capture discriminative features for deraining. Second, to further improve the generalization ability of FBL for rain streaks in various scales, we add cross-scale self-supervision to regularize the network training. The constraint forces the extracted features of inputs in different scales to be equivalent after rescaling. Therefore, FBL can offer similar responses based on solely image content without the interleave of scale and is capable to remove rain streaks in various scales. Extensive experiments in quantitative and qualitative evaluations demonstrate the superiority of our FBL for rain streak removal, especially for the real cases where very large rain streaks exist, and prove the effectiveness of its each component. Our code will be public available at: https://github.com/flyywh/AAAI-2020-FBL-SS.
Detection of Stroke Disease using Machine Learning Algorithms

A stroke is a medical condition in which poor blood flow to the brain results in cell death. It is now a day a leading cause of death all over the world. Several risk factors believe to be related to the cause of stroke has been found by inspecting the affected individuals. Using these risk factors, a number of works have been carried out for predicting and classifying stroke diseases. Most of the models are based on data mining and machine learning algorithms. In this work, we have used four machine learning algorithms to detect the type of stroke that can possibly occur or occurred form a person's physical state and medical report data. We have collected a good number of entries from the hospitals and use them to solve our problem. The classification result shows that the result is satisfactory and can be used in real time medical report. We believe that machine learning algorithms can help better understanding of diseases and can be a good healthcare companion.
Iterative Scale-Invariant Feature Transform for Remote Sensing Image Registration

Due to significant geometric distortions and illumination differences, developing techniques for high precision and robust multisource remote sensing image registration poses a great challenge. This article presents an iterative image registration approach, called iterative scale-invariant feature transform (ISIFT) for remote sensing images, which extends the traditional scale-invariant feature transform (SIFT)-based registration system to a close-feedback SIFT system that includes a rectification feedback loop to update rectified parameters in an iterative manner. Its key idea uses consistent feature point sets obtained by maximum similarity to calculate new alignment parameters to rectify the current sensed image and the resulting rectified sensed image is then fed back to update and replace the current sensed image as a new sensed image to reimplement SIFT for next iteration. The same process is repeated iteratively until an automatic stopping rule is satisfied. To evaluate the performance of ISIFT, both the simulated and real images are used for experiments for the validation of ISIFT. In addition, several data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. Furthermore, experiments with different rotation are also performed to verify the adaptability of ISIFT under different rotation distortions. The experimental results demonstrate that ISIFT improves performance and produces better registration accuracy than traditional SIFT-based methods and existing state-of-the-art methods.
Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models

Seppo Enarvi, Marilisa Amoia, Miguel Del-Agua Teba, Brian Delaney, Frank Diehl, Stefan Hahn, Kristina Harris, Liam McGrath, Yue Pan, Joel Pinto, Luca Rubini, Miguel Ruiz, Gagandeep Singh, Fabian Stemmer, Weiyi Sun, Paul Vozila, Thomas Lin, Ranjani Ramamurthy. Proceedings of the First Workshop on Natural Language Processing for Medical Conversations. 2020.
External alternatives, job stress on job satisfaction and employee turnover intention

The purpose of this study was to determine the alternative external influence and job stress on employee satisfaction and to determine the effects of the alternative external influence, job satisfaction job stress on Turnover Intention on employees of PT Bank Mandiri Regional X South Sulawesi.Respondents in this study were 100 people.The analysis model used to determine the effect between variables was a structural model with the Partial Least Square (PLS) approach.The results show that external alternatives had a significant effect on job satisfaction, stress had no significant effect on job satisfaction, external alternatives had a significant effect on turnover intention, stress had no significant effect on turnover intention, and finally, job satisfaction had a significant effect on turnover intention.To reduce turnover intention, it is better to reduce employee access to information on outside market opportunities.Job market conditions outside the company had a stronger effect on turnover intention than the job satisfaction felt by Bank Mandiri employees and to increase employee job satisfaction efforts are needed to reduce stress in the form of reducing work overload, reducing conflict, giving proper responsibilities and improving development policies.
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Harmful data practices produce and perpetuate structural inequities that are compounded by the intersections of one's gender, race, ethnicity, class, sexuality, ability, and citizenship. This panel mobilizes 'critical refusal' as an organizing principle and lens for examining interlocking struggles across data domains, contexts, practices and cultures within CSCW and social computing research.
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss

This paper proposes a hierarchical loss for monocular depth estimation, which measures the differences between the prediction and ground truth in hierarchical embedding spaces of depth maps. In order to find an appropriate embedding space, we design different architectures for hierarchical embedding generators (HEGs) and explore relevant tasks to train their parameters. Compared to conventional depth losses manually defined on a per-pixel basis, the proposed hierarchical loss can be learned in a data-driven manner. As verified by our experiments, the hierarchical loss even learned without additional labels can capture multi-scale context information, is more robust to local outliers, and thus delivers superior performance. To further improve depth accuracy, a cross level identity feature fusion network (CLIFFNet) is proposed, where low-level features with finer details are refined using more reliable high-level cues. Through end-to-end training, CLIFFNet can learn to select the optimal combinations of low-level and high-level features, leading to more effective cross level feature fusion. When trained using the proposed hierarchical loss, CLIFFNet sets a new state of the art on popular depth estimation benchmarks.