Reviewerly/exHarmony huggingface dataset:
Transfer learning techniques for cross-domain analysis of posts in massive educational forums

Discussion forums are popular tools in Massive Open Online Courses (MOOCs), used by students to express feelings, exchange ideas, and ask for help. Due to the large number of enrolled students, several approaches to automated forum post analysis are emerging for helping instructors to moderate and plan their interventions. Such approaches have the common drawback that, when trained on posts from one course or domain, their application on another course or domain is often unsatisfactory. To solve this problem, this chapter introduces a cross-domain text categorization tool that includes transfer learning capabilities for detecting intent, sentiment, confusion, and urgency of MOOC forum posts. The tool, based on convolutional and recurrent neural networks, can be trained on a labeled dataset and then adapted to any course or domain by tuning it on a small set of labeled samples. The proposed tool has been experimented upon and compared with related works.
Horizontal-to-Vertical Video Conversion

At this blooming age of social media and mobile platform, mass consumers are migrating from horizontal video to vertical contents delivered on hand-held devices. Accordingly, revitalizing the exposure of horizontal video becomes vital and urgent, which is hereby tackled by our automated horizontal-to-vertical (abbreviated as H2V) video conversion framework. Essentially, the {\it \textbf{H2V}} framework performs subject-preserving video cropping instantiated in the proposed Rank-SS module. Rank-SS incorporates object detection to discover candidate subjects, from which we select the primary subject-to-preserve leveraging location, appearance, and salient cues in a convolutional neural network. In addition to converting horizontal videos vertically by cropping around the selected subject, automatic shot detection and multi-object tracking are integrated into the {\it \textbf{H2V}} framework to accommodate long and complex videos. To develop {\it \textbf{H2V}} systems, we collect an {\it \textbf{H2V-142K}} dataset containing 125 videos (132K frames) and 9,500 cover images annotated with primary subject bounding boxes. On {\it \textbf{H2V-142K}} and public object detection datasets, our method demonstrates promising results on the subject selection comparing to the related solutions. Furthermore, our {\it \textbf{H2V}} framework is industrially deployed hosting millions of daily active users and exhibits favorable H2V conversion performance. By making this dataset as well as our approach publicly available, we wish to pave the way for more horizontal-to-vertical video conversion research. Our collected H2V-142K dataset is available atH2V-142K website.
EAGLE: Expedited Device Placement with Automatic Grouping for Large Models

Advanced deep neural networks with large sizes are usually trained on a mixture of devices, including multiple CPUs and GPUs. The model training speed and efficiency are drastically impacted by the placement of operations on devices. To identify the optimal device placement, the state-of-the-art method is based on reinforcement learning with a hierarchical model, which partitions the operations into groups and then assigns each group to specific devices. However, due to the additional dimension of grouping decisions coupled with the placement, the reinforcement learning efficiency is greatly reduced. With modern neural networks growing in size and complexity, the issue of low efficiency and high cost in device placement is further aggravated. In this paper, we propose our design of EAGLE (Expedited Automatic Grouping for Large modEls), which integrates automatic grouping into reinforcement learning-based placement in an optimal way, to achieve the best possible training time performance for very large models. An extra RNN is introduced to transform parameters of the grouper into inputs of the placer, linking the originally separated parts together. Further optimizations have also been made in the network inputs. We have deployed and extensively evaluated EAGLE on InceptionV3, GNMT and BERT benchmarks. Compared with the state-of-the-art, the performance achieved by our design, measured by the per-step time with the resulted placement, is 2.7% and 18.7% better for GNMT and BERT, respectively. For Inception-V3, our design achieves the fastest speed in discovering the optimal placement.
Is Hidden Safe? Location Protection against Machine-Learning Prediction Attacks in Social Networks

User privacy protection is a vital issue of concern for online social networks (OSNs). Even though users often intentionally hide their private information in OSNs, since adversaries may conduct prediction attacks to predict hidden information using advanced machine learning techniques, private information that users intend to hide may still be at risk of being exposed. Taking the current city listed on Facebook profiles as a case, we propose a solution that estimates and manages the exposure risk of users’ hidden information. First, we simulate an aggressive prediction attack using advanced state-of-the-art machine learning algorithms by proposing a new current city prediction framework that integrates location indications based on various types of information exposed by users, including demographic attributes, behaviors, and relationships. Second, we study prediction attack results to model patterns of prediction correctness (as correct predictions lead to information exposures) and construct an exposure risk estimator. The proposed exposure risk estimator has the ability not only to notify users of exposure risks related to their hidden current city but can also help users mitigate exposure risks by overhauling and selecting countermeasures. Moreover, our exposure risk estimator can improve the privacy management of OSNs by facilitating empirical studies on the exposure risks of OSN users as a group. Taking the current city as a case, this work offers insight on how to protect other types of private information against machine-learning prediction attacks and reveals several important implications for both practice management and future research.
A Multi-view Clustering Approach for Analysis of Streaming Data

Data available today in smart monitoring applications such as smart buildings, machine health monitoring, smart healthcare, etc., is not centralized and usually supplied by a number of different devices (sensors, mobile devices and edge nodes). Due to which the data has a heterogeneous nature and provides different perspectives (views) about the studied phenomenon. This makes the monitoring task very challenging, requiring machine learning and data mining models that are not only able to continuously integrate and analyze multi-view streaming data, but also are capable of adapting to concept drift scenarios of newly arriving data. This study presents a multi-view clustering approach that can be applied for monitoring and analysis of streaming data scenarios. The approach allows for parallel monitoring of the individual view clustering models and mining view correlations in the integrated (global) clustering models. The global model built at each data chunk is a formal concept lattice generated by a formal context consisting of closed patterns representing the most typical correlations among the views. The proposed approach is evaluated on two different data sets. The obtained results demonstrate that it is suitable for modelling and monitoring multi-view streaming phenomena by providing means for continuous analysis and pattern mining.
Aspect-Based Capsule Network With Mutual Attention for Recommendations

Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes. Recently, mainstream recommendation approaches have been using deep learning techniques to utilize review text. These efforts employ an affinity matrix to model the correlations between users and items and in turn aggregate the contextual features of users and items to form latent representations with some interpretability. However, constructing this simple interaction does not capture the complex correlations between users and items. Furthermore, a single representation of latent features is not sufficient to express user preferences and item attributes in reviews with multiple granularities. To this end, we propose an aspect-based capsule network and mutual attention for recommendation systems, named Acapnet. We design a novel contextual mutual attention mechanism for modeling the fine-grained correlations in the contextual features of users and items. We employ reverse dynamic routing to aggregate user and item context features into aspect features (capsules) for rating predictions; the vector length of a capsule can be used as a basis for estimating aspect importance. Extensive experiments are conducted on five real-world datasets with different features. The results show that the proposed Acapnet is superior to recent state-of-the-art models in terms of performance. Further analysis reveals that our model effectively mitigates the homogeneity among various aspect capsules with strong interpretability. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Impact Statement—On e-commerce platforms such as Amazon and Yelp, recommendation systems play a key role as a filtering technology that helps consumers find the best product or service among many choices. However, recent studies have shown that although it can provide relatively accurate services, it often fails to explain why that product is recommended to the user. The model we propose in this article, Acapnet, effectively overcomes the limitation. Compared to state-of-the-art recommendation methods, Acapnet not only outperforms in terms of performance, but also offers some superiority on interpretability.
Unit Test Smells and Accuracy of Software Engineering Student Test Suites

With an increasing emphasis on unit testing in computer science curricula, we examined students' work on testing assignments to investigate their adoption of test smells---practices that indicate potential problems in unit tests. We discovered three common causes of test smells in students' unit tests: multiple member function calls, multiple assertions, and conditional logic. We also explored how each might be associated with test inaccuracies.
Rotation Augmentation for Head Pose Estimation Problem

The head pose estimation problem is one of the essential tasks of computer vision. Currently, many solutions use neural networks and show good results. Using data augmentations can significantly improve the quality of the trained neural network. Augmentations that affect the head rotation angles can increase the representativeness of head positions in the dataset. Horizontal flipping and rotating by a certain angle can achieve the desired effect. The rotation data augmentation is well known for the detection problem, but the only horizontal flip was used for the head pose estimation problem in previous papers. This paper proposes a modification of the image rotation augmentation for the head pose estimation problem, which corrects head rotation angles. Formulas for calculating the corrected rotation angles and bounding box are given in this paper. We conducted a series of computational experiments to test the effectiveness of the proposed augmentation. This paper compares proposed augmentation with different angles rotation and baseline, which applies only horizontal flip. The 300W - LP dataset was taken as a training dataset and the AFLW2000 dataset as a validation dataset. The pretrained Resnet50 was taken as a backbone. As the head branch for each angle, regression via classification with bin size equal to one was used. The best result is achieved by rotating at a random angle from -40 to 40 degrees. The accuracy of prediction by MAE metric angles was 3.85, which is close to the result of the best SOTA method with 3.83. The improvement in the accuracy of angle prediction relative to baseline is 22.1 % according to the MAE metric.
Binary Classification With XOR Queries: Fundamental Limits and an Efficient Algorithm

We consider a query-based data acquisition problem for binary classification of unknown labels, which has diverse applications in communications, crowdsourcing, recommender systems and active learning. To ensure reliable recovery of unknown labels with as few number of queries as possible, we consider an effective query type that asks “group attribute” of a chosen subset of objects. In particular, we consider the problem of classifying m binary labels with XOR queries that ask whether the number of objects having a given attribute in the chosen subset of size d is even or odd. The subset size d, which we call query degree, can be varying over queries. We consider a general noise model where the accuracy of answers on queries changes depending both on the worker (the data provider) and query degree d. For this general model, we characterize the information-theoretic limit on the optimal number of queries to reliably recover m labels in terms of a given combination of degree-d queries and noise parameters. Further, we propose an efficient inference algorithm that achieves this limit even when the noise parameters are unknown.
Collection of plastic bottles by reverse vending machine using object detection technique

A reverse vending machine (RVM) works opposite to that of a vending machine, which takes the used water bottles, soft drink bottles into it. Verification is done whether they are plastic bottles or not using object detection technique based on pre-trained values. After the detection, the bottles are stored in the machine using conveyer belt. Here in this paper, detection and training using haar cascade classifier. Use of Sensors like a photoelectric sensor, IR spectrometers, and barcode readers makes the machine expensive. Modern technologies like Object detection made the RVM non-expensive compared to existing ones. In this digital payment era, customers can get their reward for the bottles in the form of coupons to their respective email or SMS number. The GUI gives them the option of choosing email or SMS and enter the details. The bottles in the storage are later sent to the recycling units.
PACE: Learning Effective Task Decomposition for Human-in-the-loop Healthcare Delivery

Human-in-the-loop data analysis involves both machine learning models and humans in analytic tasks. In healthcare applications, human-in-the-loop data analysis is crucial in that the model can handle "easy" tasks and hand over "hard" ones to medical experts for assistance and medical judgment, where easy tasks are the ones for which the model can provide high accuracy and hard tasks vice versa. In this process, how to decompose tasks in an effective manner is an important stage. To achieve task decomposition, classification with a reject option is a solution. However, existing studies either directly implement a reject option or dive into the theoretical details of the rejection mechanism. Different from such studies, we aim to optimize general classifiers with a reject option and hence, optimize task decomposition for healthcare applications.
Optimised Misalignment Correction from Cine MR Slices Using Statistical Shape Model

Cardiac magnetic resonance (CMR) imaging is a valuable imaging technique for the diagnosis and characterisation of cardiovascular diseases. In clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, limiting its accuracy in 3D analysis. One of the major issues for 3D reconstruction of human heart surfaces from CMR slices is the misalignment between heart slices, often arising from breathing or subject motion. In this regard, the objective of this work is to develop a method for optimal correction of slice misalignments using a statistical shape model (SSM), for accurate 3D modelling of the heart. After extracting the heart contours from 2D cine slices, we perform initial misalignment corrections using the image intensities and the heart contours. Next, our proposed misalignment correction is performed by first optimally fitting an SSM to the sparse heart contours in 3D space and then optimally aligning the heart slices on the SSM, accounting for both in-plane and out-of-plane misalignments. The performance of the proposed approach is evaluated on a cohort of 20 subjects selected from the UK Biobank study, demonstrating an average reduction of misalignment artifacts from $$1.14 \pm 0.23$$ mm to $$0.72 \pm 0.11$$ mm, in terms of distance from the final reconstructed 3D mesh.
The Ultimate DataFlow for Ultimate SuperComputers-on-a-Chip, for Scientific Computing, Geo Physics, Complex Mathematics, and Information Processing

This paper introduces a conceptual 100BillionTransistor (100BT) SuperComputers-on-a-Chip consisting of N big multi-core processors, 1000N small many-core processors, and two hardware accelerators - an ASIC TPU-like fixed-structure systolic array accelerator and a FPGA based flexible-structure re-programmable accelerator for bandwidth-bound and latency-critical Machine Learning applications respectively. The proposed SuperComputers-on-a-chip concept requires interfaces to specific external accelerators based on Quantum, Optical, Molecular, and Biological paradigms (programmable using EnergyFlow programming models - Energy Flow also representing a concept introduced in this paper) but these issues are outside the scope of this article. Keywords - Accelerators, Big Data, ControlFlow, DataFlow, ManyCore, Machine Learning, MultiCore, Systolic Array
Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs

In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only consider the historical behaviors of users, but ignore various kinds of auxiliary information, which are also critical for user embedding. In addition, traditional recommendation models only consider the immediate user response to the recommended items, and do not explicitly consider the long-term interests of users. To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users. To consider more auxiliary information of users, we construct a heterogeneous information network among users, courses, and concepts, and use a meta-path-based method which can automatically identify useful meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. Comprehensive experiments and analyses on a real-world dataset collected from XuetangX show that our proposed model outperforms some state-of-the-art methods.
Federation of AAL &amp; AHA systems through semantically interoperable framework

Ambient Assisted Living (AAL) and Active and Healthy Ageing (AHA) immensely benefit from IoT application.The federation of IoT platforms can multiply the benefits obtained by the operation of those systems in an isolated way, as it enables important synergies (e.g., intelligent information sharing, system cooperation, service enhancement).This federation requires the enablement of interoperability between the IoT systems, which represents a major challenge, as systems typically follow very different standards, data formats, semantic models and manners of representing the information.We have provided a technical solution in the frame of ACTIVAGE, a project that aims to federate multiple heterogeneous IoT platforms and systems associated to clusters of AHA Smart Homes in 12 regions across Europe, with the goal to improve the AHA service provided and create the first European AHA ecosystem.Our technical solution allows the enablement of full semantic interoperability across heterogeneous platforms and it has been validated in a test scenario.It enables significant AHA service enhancement within the ACTIVAGE ecosystem, as native applications from one platform could be used indistinctly by all federated platforms.Our solution allows good scalability federating new platforms, with linear and relatively low effort.
Conditional information gain networks as sparse mixture of experts

Deep neural network models owe their representational power and high performance in classification tasks to the high number of learnable parameters. Running deep neural network models in limited-resource environments is a problematic task. Models employing conditional computing aim to reduce the computational burden while retaining model performance on par with more complex neural network models. This paper, proposes a new model, Conditional Information Gain Networks as Sparse Mixture of Experts (sMoE-CIGNs). A CIGN model is a neural tree that allows conditionally skipping parts of the tree based on routing mechanisms inserted into the architecture. These routing mechanisms are based on differentiable Information Gain objectives. CIGN groups semantically similar samples in the leaves, enabling simpler classifiers to focus on differentiating between similar classes. This lets the CIGN model attain high classification performances with lighter models. We further improve the basic CIGN model by proposing a sparse mixture of experts model for difficult to classify samples that may get routed to suboptimal branches. If a sample has routing confidence higher than a specific threshold, the sample may be routed towards multiple child nodes. The classification decision can then be taken as a mixture of these expert decisions. We learn the optimal routing thresholds by Bayesian Optimization over a validation set by minimizing a weighted loss, including the classification accuracy and the number of multiplication and accumulations (MAC). We show the effectiveness of the CIGN models enhanced with the Sparse Mixture of Experts approach with extensive tests on MNIST, Fashion MNIST, CIFAR 100 and UCI-USPS datasets, as well as comparisons with methods from the literature. sMoE-CIGN models can retain high generalization performance, on par with a thick unconditional model while keeping the operation burden at the same level with a much thinner model.1
AIoT-Cloud-Integrated Smart Livestock Surveillance via Assembling Deep Networks with Considering Robustness and Semantics Availability

In this letter, we propose a novel smart livestock surveillance system through cooperation of AIoT (artificial intelligence of things) devices and the cloud computing platform, aiming at providing semantic information via assembling deep networks with AIoT devices of limited resource. The key of the proposed system includes two designs: deep-net assembling as a semantic surveillance service and the expandable-convolutional-block neural network (ECB-Net). The first is a development architecture of the divide-and-conquer philosophy for establishing semantic surveillance systems, and this work provides a concrete instance for promoting deep-net assembling to livestock industries. The second is an AIoT device-friendly neural network for filtering insignificant camera images to achieve high robustness of smart surveillance systems. The technical details from the architecture design to optimal ECB-Net model creation are presented in related sections. Finally, we develop the prototype of the smart livestock surveillance system and deploy it by swine rooms for conducting real-world integrated tests. Testing results reveal the superior performance of our proposed smart livestock surveillance scheme.