Reviewerly/exHarmony huggingface dataset:
Multi-Label Classification of Microblogging Texts Using Convolution Neural Network

Microblogging sites contain a huge amount of textual data and their classification is an imperative task in many applications, such as information filtering, user profiling, topical analysis, and content tagging. Traditional machine learning approaches mainly use a bag of words or n-gram techniques to generate feature vectors as text representation to train classifiers and perform considerably well for many text information processing tasks. Since short texts, such as tweets, contain a very limited number of words, the traditional machine learning approaches suffer from data sparsity and curse of dimensionality problems due to feature representation using a bag of words or n-grams techniques. Nowadays, the use of feature vectors, such as word embeddings, as an input to neural networks for text classification and clustering has shown a remarkable performance gain. In this paper, we present the different neural network models for multi-label classification of microblogging data. The proposed models are based on convolutional neural network (CNN) architectures, which utilize pre-trained word embeddings from generic and domain-specific textual data sources. The word embeddings are used individually and in various combinations through different channels of CNN to predict class labels. We also present a comparative analysis of the proposed CNN models with traditional machine learning models and one of the existing CNN architectures. The proposed models are evaluated over a real Twitter dataset, and the experimental results establish their efficacy to classify microblogging texts with improved accuracy in comparison with the traditional machine learning approaches and the existing CNN models.
Sphere Generative Adversarial Network Based on Geometric Moment Matching

We propose sphere generative adversarial network (GAN), a novel integral probability metric (IPM)-based GAN. Sphere GAN uses the hypersphere to bound IPMs in the objective function. Thus, it can be trained stably. On the hypersphere, sphere GAN exploits the information of higher-order statistics of data using geometric moment matching, thereby providing more accurate results. In the paper, we mathematically prove the good properties of sphere GAN. In experiments, sphere GAN quantitatively and qualitatively surpasses recent state-of-the-art GANs for unsupervised image generation problems with the CIFAR-10, STL-10, and LSUN bedroom datasets. Source code is available at https://github.com/pswkiki/SphereGAN.
Ovum

Medical devices are moving out of the clinic and into the home. The design of these devices shapes our experience of interacting with our bodies. We attend to ovulation tracking devices that aid conception. We present Ovum; a research product that will be deployed in a long-term, qualitative study with couples trying to conceive. The contributions of this pictorial are the framing of the design space around at-home ovulation tracking devices and the presentation of our approach to this design space through working with oppositional experiential qualities by designing for fertility tracking as a shared, domestic and do-it-yourself experience.
Benchmarking on offline Handwritten Tamil Character Recognition using convolutional neural networks

Convolutional Neural Networks (CNN) are playing a vital role nowadays in every aspect of computer vision applications. In this paper we have used the state of the art CNN in recognizing handwritten Tamil characters in offline mode. CNNs differ from traditional approach of Handwritten Tamil Character Recognition (HTCR) in extracting the features automatically. We have used an isolated handwritten Tamil character dataset developed by HP Labs India. We have developed a CNN model from scratch by training the model with the Tamil characters in offline mode and have achieved good recognition results on both the training and testing datasets. This work is an attempt to set a benchmark for offline HTCR using deep learning techniques. This work have produced a training accuracy of 95.16% which is far better compared to the traditional approaches.
Fast Facial emotion recognition Using Convolutional Neural Networks and Gabor Filters

The emotions evolved in human face have a great influence on decisions and arguments about various subjects. In psychological theory, emotional states of a person can be classified into six main categories: surprise, fear, disgust, anger, happiness and sadness. Automatic extraction of these emotions from the face images can help in human computer interaction as well as many other applications. Machine learning algorithms and especially deep neural network can learn complex features and classify the extracted patterns. In this paper, a deep learning based framework is proposed for human emotion recognition. The proposed framework uses the Gabor filters for feature extraction and then a Convolutional Neural Network (CNN) for classification. The experimental results show that the proposed methodology increases both of the speed training process of CNN and the recognition accuracy.
Augmented Reality for Remote Collaboration in Aircraft Maintenance Tasks

In this paper, we present a concept study to facilitate maintenance of an operating aircraft based on its lifelong collected data, called Digital Twin. It demonstrates a damage assessment scenario on a real aircraft component. We propose a graphical user interface that contains menu-guided instructions and inspection documentation to increase the efficiency of manual processes. Furthermore, experts located at different sites can join via a virtual session. By inspecting a 3D model of the aircraft component, they can see synchronized information from a Digital Twin database. With Augmented Reality glasses, the Microsoft HoloLens, a Digital Twin can be experienced personally. In the inspector's view, the 3D model of the Digital Twin is directly superimposed on the physical component. This Mixed Reality Vision can be used for inspection purposes. Any inspection related information can be directly attached to the component. For example, damage locations are marked by the inspector on the component's surface and are stored in the Digital Twin database. Our scenario demonstrates how new information can be derived from the combination of collected data and analyses from the Digital Twin database. This information is used to maintain the continued airworthiness of the aircraft. Feedback from domain related engineers confirm that our interface has an enormous potential for solving current maintenance problems in the aviation industry. Additionally, our study provides ideas for the integration of further analysis functions into the interface.
Audio Word2vec: Sequence-to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation

In text, word2vec transforms each word into a fixed-size vector used as the basic component in applications of natural language processing. Given a large collection of unannotated audio, audio word2vec can also be trained in an unsupervised way using a sequence-to-sequence autoencoder (SA). These vector representations are shown to effectively describe the sequential phonetic structures of the audio segments. In this paper, we further extend this research in the following two directions. First, we disentangle phonetic information and speaker information from the SA vector representations. Second, we extend audio word2vec from the word level to the utterance level by proposing a new segmental audio word2vec in which unsupervised spoken word boundary segmentation and audio word2vec are jointly learned and mutually enhanced, and utterances are directly represented as sequences of vectors carrying phonetic information. This is achieved by means of a segmental sequence-to-sequence autoencoder, in which a segmentation gate trained with reinforcement learning is inserted in the encoder.
Personalized Scientific Paper Recommendation Based on Heterogeneous Graph Representation

The accelerating rate of scientific publications makes it extremely difficult for researchers to find out the relevant papers and related works.Recommender systems that aim at solving the information overload problem have attracted lots of attention.However, existing paper recommendation works generally rely on the simple citation-ships between papers, which ignore the heterogeneity of the academic graphs.In this paper, we solve the personalized paper recommendation problem in the setting of heterogeneous information networks.A heterogeneous graph representation based recommendation method named HGRec is proposed.First, the author and paper profiles are constructed based on the extracted contents information.Second, we initialize the node vectors by employing the word-embedding technique.Third, we jointly update the node embeddings in the heterogeneous graph by proposing two meta-path based proximity measures.Finally, the paper recommendation is completed by calculating the similarity of the generated author and paper feature vectors.We present experiments on a real academic network, the DBLP network.The comparative results demonstrate the effectiveness of the proposed personalized recommendation approach compared to state-of-the-art methods.
Human Activity Recognition on Smartphones Using a Bidirectional LSTM Network

Thanks to the technological development on the last decades, inertial sensors have reduced their size to the point of occupying a small space inside a smartphone. The information of these sensors has been exploited in order to recognize human activities using different approaches, especially using machine learning techniques. This paper presents a human activity recognition (HAR) system that uses accelerometer and gyroscope data obtained from a smartphone as inputs to a bidirectional long short-term memory (LSTM) network. Six human activities were recognized: sitting, standing, laying, walking, walking upstairs, and walking downstairs. Different network architectures were tested using a grid search methodology. We have found that the easiest activity to recognize was laying down whereas the most difficult activity to recognize was sitting. Thanks to the property of bidirectional LSTM networks to process past and future information of a signal, walking downstairs and walking upstairs (two related activities) were recognized correctly. An overall accuracy of 92.67% was obtained using the proposed HAR system.
Application Programming Interface (API) Research

The purpose of this study is to perform a synthesis of API research. The study took stock of literature from academic journals on APIs with their associated themes, frameworks, methodologies, publication outlets and level of analysis. The authors draw on a total of 104 articles from academic journals and conferences published from 2010 to 2018. A systematic literature review was conducted on the selected articles. The findings suggest that API research is primarily atheoretical and largely focuses on the technological dimensions such as design and usage; thus, neglecting most of the social issues such as the business and managerial applications of APIs, which are equally important. Future research directions are provided concerning the gaps identified.
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions

Amirreza Shirani, Franck Dernoncourt, Paul Asente, Nedim Lipka, Seokhwan Kim, Jose Echevarria, Thamar Solorio. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
Displaying Product Manufacturing Information in Augmented Reality for Inspection

Model-based definition and augmented reality are two emerging technologies, which enable a faster product development cycle and better dimensional control. They are an enabler of industry 4.0, and a better way to transfer the data to the user. Although each of the technologies is well researched, their combination presents a challenge from a research perspective. Model-based definition enables the product manufacturing data to be used in downstream processes, which include quality control and inspection. We developed and presented a method that enables the transfer of product manufacturing information from a 3D model and displays the graphical presentation to the user in augmented reality. The presented data is expected to aid the user with performing quality control more effectively, which needs to be tested.
What do Developers Know About Machine Learning: A Study of ML Discussions on StackOverflow

Machine learning, a branch of Artificial Intelligence, is now popular in software engineering community and is successfully used for problems like bug prediction, and software development effort estimation. Developers' understanding of machine learning, however, is not clear, and we require investigation to understand what educators should focus on, and how different online programming discussion communities can be more helpful. We conduct a study on Stack Overflow (SO) machine learning related posts using the SOTorrent dataset. We found that some machine learning topics are significantly more discussed than others, and others need more attention. We also found that topic generation with Latent Dirichlet Allocation (LDA) can suggest more appropriate tags that can make a machine learning post more visible and thus can help in receiving immediate feedback from sites like SO.
Interact and Decide

This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challenging task, in which intricate group dynamics have to be considered. As such, this is to be contrasted with the standard recommendation problem where recommendations are personalized with respect to a single user. Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i.e., a user's decision is highly dependent on the other group members. All in all, our key motivation manifests in a form of an attentive neural model that captures fine-grained interactions between group members. In our MoSAN model, each sub-attention module is representative of a single member, which models a user's preference with respect to all other group members. Subsequently, a Medley of Sub-Attention modules is then used to collectively make the group's final decision. Overall, our proposed model is both expressive and effective. Via a series of extensive experiments, we show that MoSAN not only achieves state-of-the-art performance but also improves standard baselines by a considerable margin.
Data-Driven Photovoltaic Generation Forecasting Based on a Bayesian Network With Spatial–Temporal Correlation Analysis

Spatiotemporal analysis has been recognized as one of the most promising techniques to improve the accuracy of photovoltaic (PV) generation forecasts. In recent years, PV generation data of a number of PV systems distributed in a geographical locale have become increasingly available. This paper conducts a thorough investigation of the spatial-temporal correlation amongst PV generation data of distributed PV systems. PV generation data of different PV systems located at different sites may exhibit similar time varying patterns. To quantify such spatial correlation, a suitable spatial similarity metric is chosen and its applicability is examined. To evaluate the temporal correlations amongst the PV generation data collected from distributed PV systems, a shape-based distance metric is proposed. A data-driven inference model, built on a Bayesian network, is developed for a very short-term PV generation forecast (less than 30 min). The model utilizes historic PV generation and weather data, and incorporates the abovementioned spatial similarity and temporal correlation to support the PV output forecast. The experiment results show that the proposed method achieves a promising performance compared to a number of baseline methods.
Artificial search agents with cognitive intelligence for binary optimization problems

Artificial intelligence techniques bring about new opportunities in problem solving. The notion such techniques have in common is learning mechanisms that are mostly problem and environment dependent. Although optimality is not guaranteed by these techniques, they draw attention due to being able to solve challenging optimization problems efficiently. Accordingly, the present study introduces a swarm-based optimization algorithm that is comprised of artificial search agents each with individual cognitive intelligence. In this technique, each agent is allowed to learn from problem space individually. Therefore, each of the search agents exhibits a different search characteristic. Nevertheless, they occasionally share information of the promising regions with each other. Thus, central swarm intelligence is also allowed to lead those independent search agents. Moreover, information-sharing techniques in the developed algorithm are designed as adaptive procedures so that search agents learn throughout generations by avoiding premature convergence and local optima problems as much as possible. The performance of the proposed algorithm is tested on a set of binary optimization problems including the set-union knapsack problem and the uncapacitated facility location problem, which have numerous real-life applications. All reported benchmarking problems are solved by the developed algorithm. As demonstrated by the comprehensive computational study and statistical tests, the proposed swarm-based algorithm significantly improves most of the published results.
Group Sampling for Scale Invariant Face Detection

Detectors based on deep learning tend to detect multi-scale faces on a single input image for efficiency. Recent works, such as FPN and SSD, generally use feature maps from multiple layers with different spatial resolutions to detect objects at different scales, e.g., high-resolution feature maps for small objects. However, we find that such multi-layer prediction is not necessary. Faces at all scales can be well detected with features from a single layer of the network. In this paper, we carefully examine the factors affecting face detection across a large range of scales, and conclude that the balance of training samples, including both positive and negative ones, at different scales is the key. We propose a group sampling method which divides the anchors into several groups according to the scale, and ensure that the number of samples for each group is the same during training. Our approach using only the last layer of FPN as features is able to advance the state-of-the-arts. Comprehensive analysis and extensive experiments have been conducted to show the effectiveness of the proposed method. Our approach, evaluated on face detection benchmarks including FDDB and WIDER FACE datasets, achieves state-of-the-art results without bells and whistles.
Temporal Information Processing With an Integrated Laser Neuron

Spiking neural networks enable efficient information processing in real-time. Excitable lasers can exhibit ultrafast spiking dynamics, and when preceded by a photodetector in an O/E/O link, can process optical spikes at different wavelengths and thus be interconnected in large neural networks. Here, we experimentally demonstrate and numerically simulate the spiking dynamics of a laser neuron fabricated in a photonic integrated circuit. Our spiking laser neuron is shown to perform coincidence detection with nanosecond time resolution, and we observe refractory periods in the order of 0.1 ns. We propose a method to implement XOR classification using our laser neurons, and simulations of the resultant dynamics indicate robust tolerance to timing jitter.
The least secure places in the universe? A systematic literature review on information security management in higher education

Current research has demonstrated the progressively more strategic role that information security has in modern organisations. Higher education is no exception. The reported increasing number of security breaches experienced in recent years by higher education institutions epitomises the importance of confidentiality, integrity and availability of information in universities. To synthesise research in this field, this literature review systematically examines papers that have been published in the last thirteen years. The present review aims at expanding our understanding of the sub-topics, perspectives, methodologies, and trends that characterise this nascent field of investigation. Literature gaps are highlighted and an agenda for further work is proposed. First of its kind, this review concludes that information security management in higher education is a highly under-investigated topic. Areas for further research include information security culture; comparative studies on information security management in industries other than higher education; comparative studies across universities; and economics of information security management.
Training Faster by Separating Modes of Variation in Batch-Normalized Models

Batch Normalization (BN) is essential to effectively train state-of-the-art deep Convolutional Neural Networks (CNN). It normalizes the layer outputs during training using the statistics of each mini-batch. BN accelerates training procedure by allowing to safely utilize large learning rates and alleviates the need for careful initialization of the parameters. In this work, we study BN from the viewpoint of Fisher kernels that arise from generative probability models. We show that assuming samples within a mini-batch are from the same probability density function, then BN is identical to the Fisher vector of a Gaussian distribution. That means batch normalizing transform can be explained in terms of kernels that naturally emerge from the probability density function that models the generative process of the underlying data distribution. Consequently, it promises higher discrimination power for the batch-normalized mini-batch. However, given the rectifying non-linearities employed in CNN architectures, distribution of the layer outputs show an asymmetric characteristic. Therefore, in order for BN to fully benefit from the aforementioned properties, we propose approximating underlying data distribution not with one, but a mixture of Gaussian densities. Deriving Fisher vector for a Gaussian Mixture Model (GMM), reveals that batch normalization can be improved by independently normalizing with respect to the statistics of disentangled sub-populations. We refer to our proposed soft piecewise version of batch normalization as Mixture Normalization (MN). Through extensive set of experiments on CIFAR-10 and CIFAR-100, using both a 5-layers deep CNN and modern Inception-V3 architecture, we show that mixture normalization reduces required number of gradient updates to reach the maximum test accuracy of the batch-normalized model by ∼ 31%-47% across a variety of training scenarios. Replacing even a few BN modules with MN in the 48-layers deep Inception-V3 architecture is sufficient to not only obtain considerable training acceleration but also better final test accuracy. We show that similar observations are valid for 40 and 100-layers deep DenseNet architectures as well. We complement our study by evaluating the application of mixture normalization to the Generative Adversarial Networks (GANs), where "mode collapse" hinders the training process. We solely replace a few batch normalization layers in the generator with our proposed mixture normalization. Our experiments using Deep Convolutional GAN (DCGAN) on CIFAR-10 show that mixture-normalized DCGAN not only provides an acceleration of ∼ 58% but also reaches lower (better) "Fréchet Inception Distance" (FID) of 33.35 compared to 37.56 of its batch-normalized counterpart.