Reviewerly/exHarmony huggingface dataset:
Scalable Object-Oriented Sequential Generative Models

The most significant limitation of previous approaches to unsupervised learning for object-oriented representation is its scalability. Most of the previous models have been shown to work only on scenes with a few objects. In this paper, we propose SCALOR, a generative model for Scalable Sequential Object-Oriented Representation. With the spatially parallel attention and proposal-rejection mechanism, SCALOR is a scalable model that can deal with orders of magnitude more objects that previous models. Besides, we introduce the background model so that it can model the foreground objects and complex background together. In experiments on large-scale MNIST and DSprite datasets, we demonstrate that SCALOR can deal with scenes with near 100 objects as well as modeling complex natural background images. Importantly, using SCALOR, we demonstrate for the first time a result of modeling natural scenes with several tens of moving objects
Finding Hadamard Matrices by a Quantum Annealing Machine

Finding a Hadamard matrix (H-matrix) among the set of all binary matrices of corresponding order is a hard problem, which potentially can be solved by quantum computing. We propose a method to formulate the Hamiltonian of finding H-matrix problem and address its implementation limitation on existing quantum annealing machine (QAM) that allows up to quadratic terms, whereas the problem naturally introduces higher order ones. For an M-order H-matrix, such a limitation increases the number of variables from M2 to (M3 + M2 - M)/2, which makes the formulation of the Hamiltonian too exhaustive to do by hand. We use symbolic computing techniques to manage this problem. Three related cases are discussed: (1) finding N < M orthogonal binary vectors, (2) finding M-orthogonal binary vectors, which is equivalent to finding a H-matrix, and (3) finding N-deleted vectors of an M-order H-matrix. Solutions of the problems by a 2-body simulated annealing software and by an actual quantum annealing hardware are also discussed.
Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation

We propose an interactive-predictive neural machine translation framework for easier model personalization using reinforcement and imitation learning. During the interactive translation process, the user is asked for feedback on uncertain locations identified by the system. Responses are weak feedback in the form of keep and delete edits, and expert demonstrations in the form of substitute edits. Conditioning on the collected feedback, the system creates alternative translations via constrained beam search. In simulation experiments on two language pairs our systems get close to the performance of supervised training with much less human effort.
Human Induced Pluripotent Stem Cell Reprogramming Prediction in Microscopy Images using LSTM based RNN

We present a LSTM (Long Short-Term Memory) based RNN (recurrent neural network) method for predicting human induced Pluripotent Stem (hiPS) cells in the reprogramming process. The method uses a trained LSTM network by time-lapse microscopy images to predict growth and transition of reprogramming processes of CD34+ human cord blood cells into hiPS cells. The prediction can be visualized by output time-series probability images. The growth and transition are thus analyzed quantitatively by region areas of distinct cells emerged during the iPS formation processes. The experimental results show that our LSTM network is a potentially powerful tool to predict the cells at the distinct phases of the reprogramming to hiPS cells. This method should be extremely useful not only for basic biology of iPS cells but also detection of the reprogramming cells that will become genuine hiPS cells even at early stages of hiPS formation. Such predictive power should greatly reduce cost, labor and time required for establishment of the genuine hiPS cells, thereby accelerating the practical use of hiPS cells in regenerative medicine.
HR-CAM: Precise Localization of Pathology Using Multi-level Learning in CNNs

We propose a CNN based technique that aggregates feature maps from its multiple layers that can localize abnormalities with greater details as well as predict pathology under consideration. Existing class activation mapping (CAM) techniques extract feature maps from either the final layer or a single intermediate layer to create the discriminative maps and then interpolate to upsample to the original image resolution. In this case, the subject specific localization is coarse and is unable to capture subtle abnormalities. To mitigate this, our method builds a novel CNN based discriminative localization model that we call high resolution CAM (HR-CAM), which accounts for layers from each resolution, therefore facilitating a comprehensive map that can delineate the pathology for each subject by combining low-level, intermediate as well as high-level features from the CNN. Moreover, our model directly provides the discriminative map in the resolution of the original image facilitating finer delineation of abnormalities. We demonstrate the working of our model on a simulated abnormalities data where we illustrate how the model captures finer details in the final discriminative maps as compared to current techniques. We then apply this technique: (1) to classify ependymomas from grade IV glioblastoma on T1-weighted contrast enhanced (T1-CE) MRI and (2) to predict Parkinson’s disease from neuromelanin sensitive MRI. In all these cases we demonstrate that our model not only predicts pathologies with high accuracies, but also creates clinically interpretable subject specific high resolution discriminative localizations. Overall, the technique can be generalized to any CNN and carries high relevance in a clinical setting.
The Impact of Popularity Bias on Fairness and Calibration in Recommendation.

Recently there has been a growing interest in fairness-aware recommender systems, including fairness in providing consistent performance across different users or groups of users. A recommender system could be considered unfair if the recommendations do not fairly represent the tastes of a certain group of users while other groups receive recommendations that are consistent with their preferences. In this paper, we use a metric called miscalibration for measuring how a recommendation algorithm is responsive to users' true preferences and we consider how various algorithms may result in different degrees of miscalibration. A well-known type of bias in recommendation is popularity bias where few popular items are over-represented in recommendations, while the majority of other items do not get significant exposure. We conjecture that popularity bias is one important factor leading to miscalibration in recommendation. Our experimental results using two real-world datasets show that there is a strong correlation between how different user groups are affected by algorithmic popularity bias and their level of interest in popular items. Moreover, we show algorithms with greater popularity bias amplification tend to have greater miscalibration.
Visualizing Inertial Data For Wearable Sensor Based Daily Life Activity Recognition Using Convolutional Neural Network

Nowadays human activity recognition (HAR) plays an crucial role in the healthcare and wellness domains, for example, HAR contributes to context-aware systems like elder home assistance and care as a core technology. Despite promising performance in terms of recognition accuracy achieved by the advancement of machine learning for classification tasks, most of the existing HAR approaches, which adopt low-level handcrafted features, cannot completely deal with practical activities. Therefore, in this paper, we present an efficient wearable sensor based activity recognition method that allows encoding inertial data into color image data for learning highly discriminative features by convolutional neural networks (CNNs). The proposed data encoding technique converts tri-axial samples to color pixels and then arranges them for image-formed representation. Our method reaches the recognition accuracy of over 95% on two challenging activities datasets and further outperforms other deep learning-based HAR approaches.
Defending Neural Backdoors via Generative Distribution Modeling

Neural backdoor attack is emerging as a severe security threat to deep learning, while the capability of existing defense methods is limited, especially for complex backdoor triggers. In the work, we explore the space formed by the pixel values of all possible backdoor triggers. An original trigger used by an attacker to build the backdoored model represents only a point in the space. It then will be generalized into a distribution of valid triggers, all of which can influence the backdoored model. Thus, previous methods that model only one point of the trigger distribution is not sufficient. Getting the entire trigger distribution, e.g., via generative modeling, is a key to effective defense. However, existing generative modeling techniques for image generation are not applicable to the backdoor scenario as the trigger distribution is completely unknown. In this work, we propose max-entropy staircase approximator (MESA), an algorithm for high-dimensional sampling-free generative modeling and use it to recover the trigger distribution. We also develop a defense technique to remove the triggers from the backdoored model. Our experiments on Cifar10/100 dataset demonstrate the effectiveness of MESA in modeling the trigger distribution and the robustness of the proposed defense method.
Mask Embedding for Realistic High-Resolution Medical Image Synthesis

Generative Adversarial Networks (GANs) have found applications in natural image synthesis and begin to show promises generating synthetic medical images. In many cases, the ability to perform controlled image synthesis using masked priors such as shape and size of organs is desired. However, mask-guided image synthesis is challenging due to the pixel level mask constraint. While the few existing mask-guided image generation approaches suffer from the lack of fine-grained texture details, we tackle the issue of mask-guided stochastic image synthesis via mask embedding. Our novel architecture first encodes the input mask as an embedding vector and then inject these embedding into the random latent vector input. The intuition is to classify semantic masks into partitions before feature up-sampling for improved sample space mapping stability. We validate our approach on a large dataset containing 39,778 patients with 443,556 negative screening Full Field Digital Mammography (FFDM) images. Experimental results show that our approach can generate realistic high-resolution ( $$256\times 512$$ ) images with pixel-level mask constraints, and outperform other state-of-the-art approaches.
Human-Centered Content Design in Augmented Reality

Augmented Reality integrates digital information with a person’s environment in real time. Given its expanded use in personal and professional contexts, content strategists must pay attention to content design for augmented reality. Building on prior work from early mixed and augmented reality technical researchers, we share results from our discourse analysis with specific attention to embodiment and authenticity; attention to participatory design with focus on empathy, accessibility, and usability; and focus on audience experience and immersion. From this examination we propose a set of heuristics for creating human-centered content amid the dynamic and highly evolving spectrum of augmented reality.
Structured Knowledge Distillation for Dense Prediction

In this work, we consider transferring the structure information from large networks to compact ones for dense prediction tasks in computer vision. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the distillation scheme for image classification and perform knowledge distillation for each pixel separately, leading to sub-optimal performance. Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem. Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pair-wise similarities by building a static graph; and ii) holistic distillation that uses adversarial training to distill holistic knowledge. The effectiveness of our knowledge distillation approaches is demonstrated by experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection. Code is available at: https://git.io/StructKD
Performance Comparison of Intrusion Detection System Between Deep Belief Network (DBN)Algorithm and State Preserving Extreme Learning Machine (SPELM) Algorithm

This paper work is focused on Performance comparison of intrusion detection system between DBN Algorithm and SPELM Algorithm. Researchers have used this new algorithm SPELM to perform experiments in the area of face recognition, pedestrian detection, and for network intrusion detection in the area of cyber security. The scholar used the proposed State Preserving Extreme Learning Machine(SPELM) algorithm as machine learning classifier and compared it's performance with Deep Belief Network (DBN) algorithm using NSL KDD dataset. The NSL- KDD dataset has four lakhs of data record; out of which 40% of data were used for training purposes and 60% data used in testing purpose while calculating the performance of both the algorithms. The experiment as performed by the scholar compared the Accuracy, Precision, recall and Computational Time of existing DBN algorithm with proposed SPELM Algorithm. The findings have show better performance of SPELM; when compared its accuracy of 93.20% as against 52.8% of DBN algorithm;69.492 Precision of SPELM as against 66.836 DBN and 90.8 seconds of Computational time taken by SPELM as against 102 seconds DBN Algorithm.
Contour feature fusion SSD Algorithm

Aiming at the missed detection problem in Single Shot MultiBox Detector (SSD), this paper proposes Contour feature fusion SSD algorithm (C-SSD). The algorithm performs fusion operation on the features layer to complement the object contour, improve expression ability of object feature information. This operation can enhance the detection capability and reduce missed detection rate. The simulation shows our C-SSD has a better performance on detecting the road participants such as vehicles, bicycles and pedestrians compared with SSD. The mAP of ours is increased by 0.78 points on the test result of PASCAL VOC2007.
CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison

In this paper, we present an OWL-based ontology, the Cloud Computing Ontology (CoCoOn), that defines concepts, features, attributes and relations to describe Cloud infrastructure services. We also present datasets that are built using CoCoOn and scripts (i.e. SPARQL template queries and web applications) that demonstrate the real-world applicability of the ontology. We also describe the design of the ontology and the architecture of related services developed with it.
Privacy Changes Everything

We are storing and querying datasets with the private information of individuals at an unprecedented scale in settings ranging from IoT devices in smart homes to mining enormous collections of click trails for targeted advertising. Here, the privacy of the people described in these datasets is usually addressed as an afterthought, engineered on top of a DBMS optimized for performance. At best, these systems support security or managing access to sensitive data. This status quo has brought us a plethora of data breaches in the news. In response, governments are stepping in to enact privacy regulations such as the EU’s GDPR. We posit that there is an urgent need for trustworthy database system that offer end-to-end privacy guarantees for their records with user interfaces that closely resemble that of a relational database. As we shall see, these guarantees inform everything in the database’s design from how we store data to what query results we make available to untrusted clients. In this position paper we first define trustworthy database systems and put their research challenges in the context of relevant tools and techniques from the security community. We then use this backdrop to walk through the “life of a query” in a trustworthy database system. We start with the query parsing and follow the query’s path as the system plans, optimizes, and executes it. We highlight how we will need to rethink each step to make it efficient, robust, and usable for database clients.
Algorithmic Regulation

This book presents interdisciplinary perspectives on algorithms which can be observed in contemporary debates. These have been gathered together under the broad notion of ‘algorithmic regulation’. This introductory chapter develops the book’s argument in a number of steps. First, it explores the meaning of ‘algorithmic regulation’. Second, it reflects on how debates regarding algorithmic regulation relate to wider debates about the interaction between technology and regulation. Third, it considers algorithmic systems as potentially novel types of large technical systems. In doing so, it highlights the growing consensus regarding critical challenges for the regulation and governance of algorithmic systems, but also notes the lack of agreement regarding potential solutions. It concludes with an overview of the subsequent chapters.
The Influence of Topography to the distribution of Ni-laterite deposits of Mangguruh Area, Sebuku Island, South Kalimantan

Abstract One important factor that influences the optimization of the formation of nickel laterite deposits is the slope of a region's topography. The Mangguruh area in Sebuku Island in South Kalimantan is composed of ultramafic rocks on a gently flat topography but forms laterite deposits of varying thickness. Based on the exploration data from 33 drilling points and field data acquisition, this study aims to: 1) create topographic slope groups with varying ranges to determine the range most suited to real conditions in the field, 2) determine the laterite thickness, 3) analyze the concentration of Ni and Fe in limonite and saprolite layers, 4) analyze the effect of topographic slope to the laterite thickness. The method used is the measurement of topographic slope in the field with compass and using ArcGIS software applications in preparing the maps, analyzing laterite profiles from the drilling data and sampling rocks and ores. The study performed a laboratory analysis consisting of petrographic analysis and X-ray Fluorescence testing of the elements. The result revealed that the appropriate topographic slope groups in the study area were 0-5%, 5-10%, 10-15% and ≥ 15%. The laterite thickness of &gt; 10m lied on the slope of 0-5% and 5-10% and contains a moderate-high nickel content of 1.2% -&gt; 1.4%. This indicates that laterite nickel enrichment is influenced by topography. While the distribution of iron ore with medium and high levels of concentration (45-50% and &gt; 50%) has commonly occurred in each topographic slope group indicating that topography is less influential on the distribution of iron ore. Earlier research has indicated that effect of topography on different morphologies is clear and many of the studies were conducted on a regional scale, but interestingly in Sebuku area, the topography is gently flat but the thickness and nickel content is varied. So the results of this study can be applied to slightly flat slope areas and therefore needs more detailed mapping.
Inclusion of Research Materials When Submitting an Article to <i>Language Learning</i>

Abstract Building on initiatives to promote high quality methodologies and Open Science practices in the language sciences, Language Learning will request, as of January 1, 2020, that all submissions to the journal include, whenever possible, the full materials used in the study for peer review. This includes materials used to elicit and code primary and secondary data (such as questionnaires, language tests, interview or observation schedules, and coding schemas). These materials will be shared with reviewers to better inform the peer review process and ensure rigorous evaluation of the methods used. If the manuscript is accepted, authors will then be encouraged to make their materials available on an open, sustainable repository, though there is no requirement to do so. In this Editorial, we outline the benefits of this policy for the advancement of the language sciences and discuss some potential concerns that authors may have.
Towards High Accuracy Named Entity Recognition for Icelandic.

We report on work in progress which consists of annotating an Icelandic corpus for named entities (NEs) and using it for training a named entity recognizer based on a Bidirectional Long Short-Term Memory model. Currently, we have annotated 7,538 NEs appearing in the first 200,000 tokens of a 1 million token corpus, MIM-GOLD, originally developed for serving as a gold standard for part-of-speech tagging. Our best performing model, trained on this subset of MIM-GOLD, and enriched with external word embeddings, obtains an overall F1 score of 81.3% when categorizing NEs into the following four categories: persons, locations, organizations and miscellaneous. Our preliminary results are promising, especially given the fact that 80% of MIM-GOLD has not yet been used for training.
Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection

Large labeled datasets are required for training a powerful semantic segmentation model. However, it is very expensive to construct pixel-wise annotated images. In this work, we propose a general batch mode active learning algorithm for semantic segmentation which automatically selects important samples to be labeled for building a competitive classifier. In our approach the edge information of an image is first introduced as a new selecting clue of active learning, which can measure the essential information relevant to segmentation performance. In addition, we also incorporate the informativeness based on Query by Committee (QBC) and representativeness criteria in our algorithm. We combine three clues to select a batch of samples during each iteration. It is shown that the image edge information is significant for the active learning for semantic segmentation in the experiments. And we also demonstrate the performance of our method outperforms the state of the art active learning approaches on the datasets of CamVid, Stanford Background and PASCAL VOC 2012.
GDPR Security and Confidentiality compliance in LMS' a problem analysis and engineering solution proposal

We have studied the main Learning Management Systems (LMSs) to comprehend how personal data is processed and stored. We found that all the users' personal information, activity, and logs are stored unencrypted on the server filesystem and databases. A user with access to such resources may have full access to all the personal information and meta-information stored. Therefore, the LMSs are very vulnerable to information leaks in front of targeted hacker attacks due to weak GDPR compliance. In this paper, we analyze this problem from a technical and operational perspective for the open-source market leader LMS Moodle, and we propose a solution and a prototype of implementation.