Reviewerly/exHarmony huggingface dataset:
Band reordering heuristics for lossless satellite image compression with 3D-CALIC and CCSDS

Remote sensing satellite images are used widely in space imaging applications as they collect significant information of ground objects through capturing the ground surface in immense wavelength bands. The size of these images is typically enormous in quantity due to the bulky number of capturing wavelengths. The images need to transmit to the ground from the sensors for a specific application. Thus, the efficient compression techniques are required to fit the available bandwidth for reducing the transmission time. The data in the images are usually redundant spatially, spectrally and temporally which give an ample opportunity to compress the images in various domains. Most importantly, the data features have a strong correlation in the separate spectral area. As a result, the similarity-based band reordering strategy is used to increase the compression performance in comparison to the image having natural band order. However, finding the optimal band reordering is still a computationally challenging problem. In this paper, three different methods namely Band Reordering based on Consecutive Continuity Breakdown Heuristics (BRCCBH), Band Reordering based on Weighted-Correlation Heuristic (BRWCH) and Segmented BRCCBH have been proposed for the compression of multispectral, hyperspectral and hyperspectral sounder data. The presented methods are different on the number and type of heuristics used for obtaining the optimal band reordering. The performances of the proposed band reordering methods are tested using CCSDS 123 lossless predictor and lossless 3D-CALIC. The experimental results show the significant improvement on compression performance by using the proposed band ordering techniques for different types of real multispectral data (3–5% using CCSDS and 2–3% using 3D-CALIC), hyperspectral data (0.2–0.7% using CCSDS and 0.8–1% using 3D-CALIC) and hyperspectral sounder data (5.5–7% using CCSDS and 4–5% using 3D-CALIC).
Augmented reality‐based virtual manipulatives versus physical manipulatives for teaching geometric shapes to preschool children

Abstract The aim of the current study is to compare the effect of virtual and physical manipulatives on preschool children’s understanding of geometric shapes. A quasi‐experimental research design was employed where children in the experimental group used tablet computers to access Augmented Reality (AR)‐based virtual manipulatives, while children in the control group used physical manipulatives. The instruction lasted for a period of four weeks at a public primary school in Turkey with 72 children aged five and six years old. Comparison of their understanding of geometric shapes was based on their scores from pre‐test and post‐test measures of the Geometric Shape Recognition Task instrument. Analysis of the collected data revealed no statistically significant difference between the groups in the circle recognition task, while statistically significant differences were found between the groups in the recognition tasks for triangle, rectangle, and square shapes in favour of the experimental group. In addition to this, although there was an increase in the total scores of both groups, the results showed a statistically significant difference in test scores in favour of the experimental group. The current study also showed that the children had difficulty in categorising geometric shapes when their attributes were changed, therefore providing different forms of geometric shapes to children is very important for improving their conceptual understanding.
Accepted and Emerging Roles of Academic Libraries in Supporting Research 2.0

This paper identifies some of the tasks and roles that academic libraries have to fulfil in order to react to the appearance of Research 2.0 that materialises in data intensive research and requires supporting activities. Reacting to the appearance of Research 2.0 by becoming service providers for scholars working on data-intensive tasks will become an imperative for libraries worldwide, even though due to the differences between countries and institutions, the tasks described in this paper may not seem urgent today. On the other hand, the issues, we identified are already part of everyday best practices in several institutions. Some of them are fairly recent or have taken new characteristics. A few roles identified in this paper are on their way to become standard occupation, while there are still ones that require innovative approaches. Our argument is based on a non-exhaustive review of the recent literature, reporting both on theoretical and practical issues and presenting the results of empirical research in the field.
Spatiotemporal distilled dense-connectivity network for video action recognition

Two-stream convolutional neural networks show great promise for action recognition tasks. However, most two-stream based approaches train the appearance and motion subnetworks independently, which may lead to the decline in performance due to the lack of interactions among two streams. To overcome this limitation, we propose a Spatiotemporal Distilled Dense-Connectivity Network (STDDCN) for video action recognition. This network implements both knowledge distillation and dense-connectivity (adapted from DenseNet). Using this STDDCN architecture, we aim to explore interaction strategies between appearance and motion streams along different hierarchies. Specifically, block-level dense connections between appearance and motion pathways enable spatiotemporal interaction at the feature representation layers. Moreover, knowledge distillation among two streams (each treated as a student) and their last fusion (treated as teacher) allows both streams to interact at the high level layers. The special architecture of STDDCN allows it to gradually obtain effective hierarchical spatiotemporal features. Moreover, it can be trained end-to-end. Finally, numerous ablation studies validate the effectiveness and generalization of our model on two benchmark datasets, including UCF101 and HMDB51. Simultaneously, our model achieves promising performances.
Effect of impact factor and discipline on journal data sharing policies

Data sharing is crucial to the advancement of science because it facilitates collaboration, transparency, reproducibility, criticism, and re-analysis. Publishers are well-positioned to promote sharing of research data by implementing data sharing policies. While there is an increasing trend toward requiring data sharing, not all journals mandate that data be shared at the time of publication. In this study, we extended previous work to analyze the data sharing policies of 447 journals across several scientific disciplines, including biology, clinical sciences, mathematics, physics, and social sciences. Our results showed that only a small percentage of journals require data sharing as a condition of publication, and that this varies across disciplines and Impact Factors. Both Impact Factors and discipline are associated with the presence of a data sharing policy. Our results suggest that journals with higher Impact Factors are more likely to have data sharing policies; use shared data in peer review; require deposit of specific data types into publicly available data banks; and refer to reproducibility as a rationale for sharing data. Biological science journals are more likely than social science and mathematics journals to require data sharing.
Attentive and Adversarial Learning for Video Summarization

This paper aims to address the video summarization problem via attention-aware and adversarial training. We formulate the problem as a sequence-to-sequence task, where the input sequence is an original video and the output sequence is its summarization. We propose a GAN-based training framework, which combines the merits of unsupervised and supervised video summarization approaches. The generator is an attention-aware Ptr-Net that generates the cutting points of summarization fragments. The discriminator is a 3D CNN classifier to judge whether a fragment is from a ground-truth or a generated summarization. The experiments show that our method achieves state-of-the-art results on SumMe, TVSum, YouTube, and LoL datasets with 1.5% to 5.6% improvements. Our Ptr-Net generator can overcome the unbalanced training-test length in the seq2seq problem, and our discriminator is effective in leveraging unpaired summarizations to achieve better performance.
Global surface solar radiation and photovoltaic power from Coupled Model Intercomparison Project Phase 5 climate models

In this study, historical surface solar radiation (1850–2005) and future photovoltaic power output (2006–2100) are analyzed to investigate the spatial distribution and long-term variation in global solar energy based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) models and the Global Energy Balance Archive (GEBA) database. The results show that global mean surface solar radiation significantly decreased by 0.014 W m−2 year−1 in 1850–2005. According to the Model for Interdisciplinary Research on Climate (MIROC5), surface solar radiation significantly decreased by 3.42 W m−2 year−1 in 1951–1992 and increased by 4.75 W m−2 year−1 in 1993–2005. Global dimming and brightening were observed before and after the 1990s, respectively. The transition of surface solar radiation from dimming to brightening in Europe and the southeastern United States was detected in the 1980s. Stations in Northeast China, Japan, Southeast Africa, the Middle East, and the west coast of India all showed renewed decreasing trends after the 1990s. The direct and indirect effects of anthropogenic aerosols and cloudiness in different periods and regions were the main causes of the changes. To better understand the utilization of global solar energy, global potential photovoltaic power outputs were estimated in future scenarios with an empirical model. Significant increases in potential photovoltaic power are expected in East Asia, Europe, Central Africa and Central America in 2006–2100. The largest increase is expected in central China, where increases are occurring at 3 kWh m−2 year−1. Significantly decreasing potential photovoltaic power is observed in North Africa, the Middle East, Central Asia and Australia. The greatest decrease is observed in the Tibetan Plateau area (approximately −3.0 kWh m−2 year−1 in 2006–2100). With respect to the global distribution of potential photovoltaic power output, large quantities of photovoltaic power are distributed in the northern and southern parts of Africa, the Middle East, the Tibetan Plateau area, the west coasts of North and South America and most of Australia. The yearly mean sum photovoltaic power in these regions is larger than 2000 kW h m−2. Due to the long-term decreasing photovoltaic power (0.67 kWh m−2 year−1) expected worldwide in 2006–2100, effective and rational utilization of solar energy is of great importance.
Distributed e-health wide-world accounting ledger via blockchain

Today’s e-health system is centralized. This system obliges all users (patients and healthcare staff) to have a high level of trust in the intermediary who stores the data. Moreover, healthcare employees must trust that their patients are providing authentic medical records and are not altering th em in order to obtain drugs for illegal purposes. The solution proposed in this paper is a new blockchain-based architecture for the creation of an e-health system. In this architecture, wireless sensor networks (WSN) will be used in association with a WSN controller v.2 to supervise patients and their testing by healthcare staff. Transactions between blockchain and WSN are made through smart contracts, removing the need for intermediaries and preventing human error. This new model facilitates a distributed ledger, creating an e-health system that is much more optimized than the current one.
CaSym: Cache Aware Symbolic Execution for Side Channel Detection and Mitigation

Cache-based side channels are becoming an important attack vector through which secret information can be leaked to malicious parties.Previous work on cache-based side channel detection, however, suffers from the code coverage problem or does not provide diagnostic information that is crucial for applying mitigation techniques to vulnerable software.We propose CaSym, a cache-aware symbolic execution to identify and report precise information about where side channels occur in an input program.Compared with existing work, CaSym provides several unique features: (1) CaSym enables verification against various attack models and cache models, (2) unlike many symbolic-execution systems for bug finding, CaSym verifies all program execution paths in a sound way, (3) CaSym uses two novel abstract cache models that provide good balance between analysis scalability and precision, and ( 4) CaSym provides sufficient information on where and how to mitigate the identified side channels through techniques including preloading and pinning.Evaluation on a set of crypto and database benchmarks shows that CaSym is effective at identifying and mitigating side channels, with reasonable efficiency.
Grammar Error Correction in Morphologically Rich Languages: The Case of Russian

Abstract Until now, most of the research in grammar error correction focused on English, and the problem has hardly been explored for other languages. We address the task of correcting writing mistakes in morphologically rich languages, with a focus on Russian. We present a corrected and error-tagged corpus of Russian learner writing and develop models that make use of existing state-of-the-art methods that have been well studied for English. Although impressive results have recently been achieved for grammar error correction of non-native English writing, these results are limited to domains where plentiful training data are available. Because annotation is extremely costly, these approaches are not suitable for the majority of domains and languages. We thus focus on methods that use “minimal supervision”; that is, those that do not rely on large amounts of annotated training data, and show how existing minimal-supervision approaches extend to a highly inflectional language such as Russian. The results demonstrate that these methods are particularly useful for correcting mistakes in grammatical phenomena that involve rich morphology.
Plan Explanations as Model Reconciliation -- An Empirical Study

Recent work in explanation generation for decision making agents has looked at how unexplained behavior of autonomous systems can be understood in terms of differences in the model of the system and the human's understanding of the same, and how the explanation process as a result of this mismatch can be then seen as a process of reconciliation of these models. Existing algorithms in such settings, while having been built on contrastive, selective and social properties of explanations as studied extensively in the psychology literature, have not, to the best of our knowledge, been evaluated in settings with actual humans in the loop. As such, the applicability of such explanations to human-AI and human-robot interactions remains suspect. In this paper, we set out to evaluate these explanation generation algorithms in a series of studies in a mock search and rescue scenario with an internal semi-autonomous robot and an external human commander. During that process, we hope to demonstrate to what extent the properties of these algorithms hold as they are evaluated by humans.
GPU accelerated t-distributed stochastic neighbor embedding

Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods which employ dimensionality reduction to two or three dimensions are often inefficient and/or ineffective for these datasets. This paper introduces t-SNE-CUDA, a GPU-accelerated implementation of t-Distributed Symmetric Neighbor Embedding (t-SNE) for visualizing datasets and models. t-SNE-CUDA significantly outperforms current implementations with 15-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for the first time, large scale visualizations of modern computer vision datasets such as ImageNet, as well as larger NLP datasets such as GloVe. From these new visualizations, we can draw a number of interesting conclusions. In addition, the performance on machine learning datasets allows us to compute t-SNE embeddings in close to real time, and we explore the applications of such fast embeddings in the domain of importance sampling for neural network training.
A new DCT-based robust image watermarking method using teaching-learning-Based optimization

Despite the passage of more than 20 years of raising the issue of watermarking by Tirkel, the researchers are still seeking to provide an approach more resistant than existing solutions. In this paper we proposed a new watermarking method that works on JPEG-YCbCr color space and the embedding operation is based on the relationships between the DCT coefficients. The JPEG-YCbCr is rescaling of YCbCr color space that has a good robustness against most of the attacks and it used in JPEG image format. Also the relationship between the DCT coefficients is stable against most of the changes in the host image. Therefore, the proposed method has more robustness compared to some other methods. On the other hand, many intelligent optimization methods are in use regarding the nature of the phenomenon simulated by the methods. Teaching-Learning-Based Optimization (TLBO) is a novel method of optimization which has become a hot issue in recent years. The algorithm works on the principle of teaching and learning, where teachers increase the knowledge of students and also the students learn from interaction among themselves. The proposed method uses TLBO which has been applied rarely so far in watermarking algorithms and it can automatically determine the embedding parameters and suitable position for inserting the watermark. Besides, in the object function of TLBO, ensuring higher imperceptibility and also robustness against Median filter and JPEG compression have been considered. According to the experimental results, the imperceptibility of watermarked images is satisfactory, and embedded watermark is extracted successfully even if the watermarked image is distorted by various attacks.
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization

Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potential for improving representation and generalization abilities of deep CNNs. However, integration of global covariance pooling into deep CNNs brings two challenges: (1) robust covariance estimation given deep features of high dimension and small sample size; (2) appropriate usage of geometry of covariances. To address these challenges, we propose a global Matrix Power Normalized COVariance (MPN-COV) Pooling. Our MPN-COV conforms to a robust covariance estimator, very suitable for scenario of high dimension and small sample size. It can also be regarded as Power-Euclidean metric between covariances, effectively exploiting their geometry. Furthermore, a global Gaussian embedding network is proposed to incorporate first-order statistics into MPN-COV. For fast training of MPN-COV networks, we implement an iterative matrix square root normalization, avoiding GPU unfriendly eigen-decomposition inherent in MPN-COV. Additionally, progressive 1x1 convolutions and group convolution are introduced to compress covariance representations. The proposed methods are highly modular, readily plugged into existing deep CNNs. Extensive experiments are conducted on large-scale object classification, scene categorization, fine-grained visual recognition and texture classification, showing our methods outperform the counterparts and obtain state-of-the-art performance.
Privacy implications of accelerometer data

Accelerometers are sensors for measuring acceleration forces. They can be found embedded in many types of mobile devices, including tablet PCs, smartphones, and smartwatches. Some common uses of built-in accelerometers are automatic image stabilization, device orientation detection, and shake detection. In contrast to sensors like microphones and cameras, accelerometers are widely regarded as not privacy-intrusive. This sentiment is reflected in protection policies of current mobile operating systems, where third-party apps can access accelerometer data without requiring security permission. It has been shown in experiments, however, that seemingly innocuous sensors can be used as a side channel to infer highly sensitive information about people in their vicinity. Drawing from existing literature, we found that accelerometer data alone may be sufficient to obtain information about a device holder's location, activities, health condition, body features, gender, age, personality traits, and emotional state. Acceleration signals can even be used to uniquely identify a person based on biometric movement patterns and to reconstruct sequences of text entered into a device, including passwords. In the light of these possible inferences, we suggest that accelerometers should urgently be re-evaluated in terms of their privacy implications, along with corresponding adjustments to sensor protection mechanisms.
The Postdigital Challenge of Critical Media Literacy

This article situates contemporary critical media literacy into a postdigital context. It examines recent advances in data literacy, with an accent to Big Data literacy and data bias, and expands them with insights from critical algorithm studies and the critical posthumanist perspective to education. The article briefly outlines differences between older software technologies and artificial intelligence (AI), and introduces associated concepts such as machine learning, neural networks, deep learning, and AI bias. Finally, it explores the complex interplay between Big Data and AI and teases out three urgent challenges for postdigital critical media literacy. (1) Critical media literacy needs to reinvent existing theories and practices for the postdigital context. (2) Reinvented theories and practices need to find a new balance between the technological aspects of data and AI literacy with the political aspects of data and AI literacy, and learn how to deal with non-predictability. (3) Critical media literacy needs to embrace the posthumanist challenge; we also need to start thinking what makes AIs literate and develop ways of raising literate thinking machines. In our postdigital age, critical media literacy has a crucial role in conceptualisation, development, and understanding of new forms of intelligence we would like to live with in the future.
Acoustic Modeling for Distant Multi-talker Speech Recognition with Single- and Multi-channel Branches

This paper presents a novel heterogeneous-input multi-channel acoustic model (AM) that has both single-channel and multi-channel input branches. In our proposed training pipeline, a single-channel AM is trained first, then a multi-channel AM is trained starting from the single-channel AM with a randomly initialized multi-channel input branch. Our model uniquely uses the power of a complemen-tal speech enhancement (SE) module while exploiting the power of jointly trained AM and SE architecture. Our method was the foundation for the Hitachi/JHU CHiME-5 system that achieved the second-best result in the CHiME-5 competition, and this paper details various investigation results that we were not able to present during the competition period. We also evaluated and reconfirmed our method's effectiveness with the AMI Meeting Corpus. Our AM achieved a 30.12% word error rate (WER) for the development set and a 32.33% WER for the evaluation set for the AMI Corpus, both of which are the best results ever reported to the best of our knowledge.