Reviewerly/exHarmony huggingface dataset:
SSN: Learning Sparse Switchable Normalization via SparsestMax

Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new perspective for deep learning: it learns to select different normalizers for different convolution layers of a ConvNet. However, SN uses softmax function to learn importance ratios to combine normalizers, leading to redundant computations compared to a single normalizer. This work addresses this issue by presenting Sparse Switchable Normalization (SSN) where the importance ratios are constrained to be sparse. Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax. SSN has several appealing properties. (1) It inherits all benefits from SN such as applicability in various tasks and robustness to a wide range of batch sizes. (2) It is guaranteed to select only one normalizer for each normalization layer, avoiding redundant computations. (3) SSN can be transferred to various tasks in an end-to-end manner. Extensive experiments show that SSN outperforms its counterparts on various challenging benchmarks such as ImageNet, Cityscapes, ADE20K, and Kinetics.
Visual Quality Evaluation for Semantic Segmentation: Subjective Assessment Database and Objective Assessment Measure

To promote the applications of semantic segmentation, quality evaluation is important to assess different algorithms and guide their development and optimization. In this paper, we establish a subjective semantic segmentation quality assessment database based on the stimulus-comparison method. Given that the database reflects the relative quality of semantic segmentation result pairs, we adopt a robust regression mapping model to explore the relationship between subjective assessment and objective distance. With the help of the regression model, we can examine whether objective metrics coincide with subjective judgement. In addition, we propose a novel relative quality prediction network (RQPN) based on Siamese CNN as a new objective metric. The metric is trained by our subjective assessment database and can be applied to evaluate the performances of semantic segmentation algorithms, even if the algorithms were not used to build the database. Experiments are conducted to show the advance and the reliability of our database and demonstrate that results predicted by RQPN are more consistent to subjective assessment than existing objective metrics.
Blockchain and IoT-based Secure Multimedia Retrieval System for a Massive Crowd

Blockchain's properties in addressing trust in highly decentralized environments can make it an enabler for novel sharing economy services. In this paper, we demonstrate the practicality of blockchain-based Secure IoT as a Service (SIoTaaS), where an IoT device can be rented from a service provider, securely and in a privacy-preserving fashion. Our framework allows the simultaneous operations of distinct providers of IoT-based sharing economy services at a large scale. Multiple parties can securely share text and multimedia in the context of location and point-of-interest sharing, perform financial transactions by hiding true identity of parties involved in various online transactions, perform user and IoT registration, transfer value transactions via Ethereum tokens between providers and consumers, as well as raw IoT data payload. This can turn smart room IoT devices, such as smart locks, light bulbs, air conditioning and fans into rentable business entities within a secure sharing economy platform. We will demonstrate such a proof of concept IoT sharing economy framework, which is specifically designed to support the temporary IoT needs of very large numbers of users, such as Hajj pilgrims concentrating for a short period of time at a single area in Saudi Arabia.
Unsupervised Dialog Structure Learning

Learning a shared dialog structure from a set of task-oriented dialogs is an important challenge in computational linguistics. The learned dialog structure can shed light on how to analyze human dialogs, and more importantly contribute to the design and evaluation of dialog systems. We propose to extract dialog structures using a modified VRNN model with discrete latent vectors. Different from existing HMM-based models, our model is based on variational-autoencoder (VAE). Such model is able to capture more dynamics in dialogs beyond the surface forms of the language. We find that qualitatively, our method extracts meaningful dialog structure, and quantitatively, outperforms previous models on the ability to predict unseen data. We further evaluate the model's effectiveness in a downstream task, the dialog system building task. Experiments show that, by integrating the learned dialog structure into the reward function design, the model converges faster and to a better outcome in a reinforcement learning setting.
Coupled Variational Recurrent Collaborative Filtering

We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Although deep neural networks have demonstrated the effectiveness of recommendation tasks, it is lack of explorations on integrating probabilistic models and deep architectures under streaming recommendation settings. Conjoining the complementary advantages of probabilistic models and deep neural networks could enhance both model effectiveness and the understanding of inference uncertainties. To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem. The framework jointly combines stochastic processes and deep factorization models under a Bayesian paradigm to model the generation and evolution of users' preferences and items' popularities. To ensure efficient optimization and streaming update, we further propose a sequential variational inference algorithm based on a cross variational recurrent neural network structure. Experimental results on three benchmark datasets demonstrate that the proposed framework performs favorably against the state-of-the-art methods in terms of both temporal dependency modeling and predictive accuracy. The learned latent variables also provide visualized interpretations for the evolution of temporal dynamics.
Adaptive Region Embedding for Text Classification

Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification. In spite of their great success, most deep learning models neglect the importance of modeling context information, which is crucial to understanding texts. In this work, we propose the Adaptive Region Embedding to learn context representation to improve text classification. Specifically, a metanetwork is learned to generate a context matrix for each region, and each word interacts with its corresponding context matrix to produce the regional representation for further classification. Compared to previous models that are designed to capture context information, our model contains less parameters and is more flexible. We extensively evaluate our method on 8 benchmark datasets for text classification. The experimental results prove that our method achieves state-of-the-art performances and effectively avoids word ambiguity.
Information security

Information security inside the organization is becoming a major issue in the modern and global world. Information accessibility and security are a major issue where user behaviour plays an important role. Information security user behaviour is becoming an increasing threat to their organization information security. Since the organization is investing in and implementing information security systems, the issue of employees’ behaviour has become increasingly important. This paper aims to show how workers treat information security and is looking upon so called “people problem”. In the research, the personal behaviour of health care professionals and workers in a Croatian production company in relation to information security was tested. Results have shown that the overall behaviour of respondents in production company is more responsible and security awareness with proper use of passwords is associated with knowledge about the importance of security application in their work. Further research is recommended.
A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization

Multilevel thresholding is to find the thresholds to segment the image with grey levels. Usually, the thresholds are so determined that some indicator functions of the segmented image are optimized. To improve the computational efficiency, we presented an optimization method for multilevel thresholding. First, the solution space is divided into subspaces. Second, the subspaces are searched to obtain their current local optimal value. Third, the subspaces that are of worse current optimal value are eliminated. Then, the next round of elimination is exerted in the remainder. The elimination is repeated until only one subspace is left and its optimal value is taken as the global optimum. In principle, any random search algorithm can be used to find the local optimum in a subspace block because it is a strategy to enhance the searching efficiency through eliminating hopeless regions as early as possible, rather than to improve the searching algorithm itself. To verify its performance, taking PSO (Particle swarm optimization) as the basic searching algorithm of subspaces, the presented method is applied to Otsu’s and Kapur’s multilevel thresholding of four different kinds of digital images. The presented method is compared with PSO, and it behaves better in efficiency.
Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

Achieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge. Several studies have used different approaches from the age of a baby to an elder adult and different datasets have been employed to measure the mean absolute error (MAE) ranging between 1.47 to 8 years. The weakness of the algorithms specifically in the borderline has been a motivation for this paper. In our approach, we have developed an ensemble technique that improves the accuracy of underage estimation in conjunction with our deep learning model (DS13K) that has been fine-tuned on the Deep Expectation (DEX) model. We have achieved an accuracy of 68% for the age group 16 to 17 years old, which is 4 times better than the DEX accuracy for such age range. We also present an evaluation of existing cloud-based and offline facial age prediction services, such as Amazon Rekognition, Microsoft Azure Cognitive Services, How-Old.net and DEX.
A relational logic for higher-order programs

Abstract Relational program verification is a variant of program verification where one can reason about two programs and as a special case about two executions of a single program on different inputs. Relational program verification can be used for reasoning about a broad range of properties, including equivalence and refinement, and specialized notions such as continuity, information flow security, or relative cost. In a higher-order setting, relational program verification can be achieved using relational refinement type systems, a form of refinement types where assertions have a relational interpretation. Relational refinement type systems excel at relating structurally equivalent terms but provide limited support for relating terms with very different structures. We present a logic, called relational higher-order logic (RHOL), for proving relational properties of a simply typed λ-calculus with inductive types and recursive definitions. RHOL retains the type-directed flavor of relational refinement type systems but achieves greater expressivity through rules which simultaneously reason about the two terms as well as rules which only contemplate one of the two terms. We show that RHOL has strong foundations, by proving an equivalence with higher-order logic, and leverage this equivalence to derive key meta-theoretical properties: subject reduction, admissibility of a transitivity rule, and set-theoretical soundness. Moreover, we define sound embeddings for several existing relational type systems such as relational refinement types and type systems for dependency analysis and relative cost, and we verify examples that were out of reach of prior work.
Dementia Patient Activity Monitoring and Fall Detection using IoT for Elderly

There is drastic increase in population of dementia patients and also elderly since we are growing as modern society.To avoid the need of special care centers for people suffering from such disease usually it is suggested to stay in their own house.The product presented in this paper provides user current localization, Real-time image processing, automatic fall detection and activity monitoring and also manage emergency situations.The Product aims to help patients retain their independence, whilst reducing the demand on care givers as well as providing patients freedom of independently walks outside.
PerspectroScope: A Window to the World of Diverse Perspectives

This work presents PerspectroScope, a web-based system which lets users query a discussion-worthy natural language claim, and extract and visualize various perspectives in support or against the claim, along with evidence supporting each perspective. The system thus lets users explore various perspectives that could touch upon aspects of the issue at hand.The system is built as a combination of retrieval engines and learned textual-entailment-like classifiers built using a few recent developments in natural language understanding. To make the system more adaptive, expand its coverage, and improve its decisions over time, our platform employs various mechanisms to get corrections from the users. PerspectroScope is available at github.com/CogComp/perspectroscope Web demo link: http://orwell.seas.upenn.edu:4002/ Link to demo video: https://www.youtube.com/watch?v=MXBTR1Sp3Bs
A Kullback–Leibler divergence-based fuzzy C-means clustering for enhancing the potential of an movie recommendation system

Recommender systems (RS) are systems that filter information and help users to choose products from a large amount of information available online. RS recommend satisfactory and useful products (items) like movies, music, books and jokes to target users that they are interested in. In collaborative filtering (CF) movie recommendation system, timeliness and accuracy are considered as an indispensable entity since it needs to aggregate the emotions, reviews and preference of users in an optimal manner for aiding them to determine suitable movies of their interest. The potential factor of accuracy and timeliness purely depends on the significance of the utilized CF methods with effective nearest neighbors for facilitating recommendation to the users. In this paper, Kullback–Leibler divergence-based fuzzy C-means clustering is proposed for enhancing the movie recommendation system. In this proposed KLD–FCM–MRS scheme, KL divergence-based cluster ensemble factor is included in the fuzzy C-means clustering methods for enhancing the stability and robustness in the clustering process. The improved sqrt-cosine similarity is also used to find the effective nearest neighbors for an active user. This proposed movie recommendation scheme is compared to the baseline approaches for investigation. The experimental results of the proposed technique confirmed a superior accuracy, recall value, mean absolute error with reduced time on par with the benchmarked schemes used for analysis.
Viewers’ Visions of the Future

The past decade has shown that new technologies can have a profound impact on how we consume television and online video content. As technologies such as VR/AR, sensors, and smart voice assistants are maturing, it is becoming pertinent to study how they could influence the next generation of TV and video experiences. While some experiments already incorporate one or more of these technologies, a systematic study into user expectations for these new technologies has not yet been conducted. In this paper, we present the results of a co-creation session resulting in two future video watching scenarios visualized using storyboards: one presenting a hyper-personalized experience based on the automatic recognition of emotions, and another one presenting an immersive experience using Virtual and Augmented Reality. We conclude with user evaluations of both concepts, offering insights in the opportunities and challenges these concepts could bring for the future of television and video experiences.
Is It Dish Washer Safe? Automatically Answering “Yes/No” Questions Using Customer Reviews

It has become commonplace for people to share their opinions about all kinds of products by posting reviews online. It has also become commonplace for potential customers to do research about the quality and limitations of these products by posting questions online. We test the extent to which reviews are useful in question-answering by combining two Amazon datasets and focusing our attention on yes/no questions. A manual analysis of 400 cases reveals that the reviews directly contain the answer to the question just over a third of the time. Preliminary reading comprehension experiments with this dataset prove inconclusive, with accuracy in the range 50-66%.
The experience of Application of Information and Communication Technologies (ICTs) as a Tool to Enhance Learning Motivation

The analytical review of foreign studies focuses on the utilization of information and communication technologies (ICT) in education. It considers the most popular networking technology currently used in the educational process in many countries of the world. It also exemplifies the studies demonstrating that the use of ICT can enhance learning motivation and improve academic performance among students of different ages.
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We explicitly determine generators of cyclic codes over a non-Galois finite chain ring Zp[u]/〈u3〉 of length pk, where p is a prime number and k is a positive integer. We completely classify that there are three types of principal ideals of Zp[u]/〈u3〉 and four types of non-principal ideals of Zp[u]/〈u3〉, which are associated with cyclic codes over Zp[u]/〈u3〉 of length pk. We then obtain a mass formula for cyclic codes over Zp[u]/〈u3〉 of length pk.
Network-theoretic information extraction quality assessment in the human trafficking domain

Information extraction (IE) is an important problem in Natural Language Processing (NLP) and Web Mining communities. Recently, IE has been applied to online sex advertisements with the goal of powering search and analytics systems that can help law enforcement investigate human trafficking (HT). Extracting key attributes such as names, phone numbers and addresses from online sex ads is extremely challenging, since such webpages contain boilerplate, obfuscation, and extraneous text in unusual language models. Assessing the quality of an IE system is an important problem that is particularly problematic in this domain due to lack of gold standard datasets. Furthermore, building a robust ground truth from scratch is an expensive and time-consuming task for social scientists and law enforcement to undertake. In this article, we undertake the empirical challenge of analyzing the quality of IE outputs in the HT domain without the provision of laboriously annotated ground truths. Specifically, we use concepts from network science to construct and study an extraction graph from IE outputs collected over a corpus of online sex ads. Our studies show that network metrics, which require no labeled ground truths, share interesting and consistent correlations with IE accuracy metrics (e.g., precision and recall) that do require ground-truths. Our methods can potentially be applied for comparing the quality of different IE systems in the HT domain without access to ground-truths.
Densely Residual Laplacian Super-Resolution

Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately.
Jointly Modeling Relevance and Sensitivity for Search Among Sensitive Content

Current search engines are designed to find what we want. But unprocessed archival collections can't be made available for search if they contain sensitive content that needs to be protected. Traditionally, content if first examined through a sensitivity review process, which becomes more difficult and time-consuming as content volumes increase. To mitigate these costs and delays, search technology should be capable of providing access to relevant content while protecting sensitive content. This paper proposes an approach that leverages learning to rank techniques. We use learning to rank to optimize a loss function that balances the value of finding relevant content with the imperative to protect sensitive content. In the experiments, a LETOR benchmark dataset, OHSUMED, is used with a subset of the MeSH labels representing the sensitive documents. Results show the efficacy of the proposed approach in comparison with some simpler baselines.
Emotion Detection in Text using Nested Long Short-Term Memory

Humans have the power to feel different types of emotions because human life is filled with many emotions.Human's emotion can be reflected through reading or writing a text.In recent years, studies on emotion detection through text has been developed.Most of the study is using a machine learning technique.In this paper, we classified 7 emotions such as anger, fear, joy, love, sadness, surprise, and thankfulness using deep learning technique that is Long Short-Term Memory (LSTM) and Nested Long Short-Term Memory (Nested LSTM).We have compared our results with Support Vector Machine (SVM).We have trained each model with 980,549 training data and tested with 144,160 testing data.Our experiments showed that Nested LSTM and LSTM give better performance than SVM to detect emotions in text.Nested LSTM gets the best accuracy of 99.167%, while LSTM gets the best performance in term of average precision at 99.22%, average recall at 98.86%, and f1-score at 99.04%.