Reviewerly/exHarmony huggingface dataset:
Efficient IoT Management With Resilience to Unauthorized Access to Cloud Storage

Cloud-based Internet of Things (IoT) management services are a promising means of ingesting data from globally dispersed devices. In this setting, it is important to regulate access to data managed by potentially untrusted cloud servers. Attribute-based encryption (ABE) is a highly effective tool for access control. However, applying ABE to IoT environments shows limitations in the following three aspects: First, the demands for storage resources increase in proportion to the complexity of the access control policies. Second, the computation cost of ABE is onerous for resource-limited devices. Lastly, ABE alone is intractable to prevent illegal key-sharing which leads to unauthorized access to data. In this article, we propose an efficient and secure cloud-based IoT data management scheme using ABE. First, we remove the storage-side dependency on the complexity of the access control policies. Second, a substantial part of computationally intensive operations is securely outsourced to the cloud servers. Lastly, unauthorized access to data via illegal key-sharing is strictly forbidden. Our security analysis and experimental results show the security and practicability of the proposed scheme.
Discriminative neural network pruning in a multiclass environment: A case study in spoken emotion recognition

Deep learning has become one of the most widely accepted paradigms regarding machine learning. It focuses on the use of hierarchical data models and builds upon the notion that in order to learn about high level data representations, a better understanding of intermediate level representation is needed. Restricted Boltzmann Machines and deep belief networks are two main types of deep learning algorithms commonly used in a wide array of classification and pattern recognition tasks. Examples of these tasks are natural language recognition, neuroimaging studies, forecasting time series, parametric voice synthesis, and speech emotion recognition among others. Recent machine learning studies suggest that deep learning networks can help map feature problems into a more advantageous position, hence improving the classification process. However, selecting a suitable Deep learning architecture in response to a specific problem can be difficult. In this study, we intend to investigate whether discriminative measures, such as Anova, Pearsonâs Correlation, Fisher score, Gain ratio, ReliefF, OneR among others, could offer pointers to identify useful neural nods in a Deep learning network. This is due to the fact that normally not all hidden neurons provide insightful information for a classification task. Our approach consists in using some of these discriminative measures to rank the hidden neurons based on their output values, and then prune them in accordance to their position within said ranking. Our results indicate that this approach is also helpful in multiclass classification problems and the pruning process seems to have a positive effect in diminishing the resulting error rate.
Classification and Comparison of Web Recommendation Systems used in Online Business

The online businesses are spreading very fast by creating personal website for their works. Due to exponential growth of dynamic and heterogeneous information over the Internet by these websites, information overload and efficiency of the system are the big challenges for the researchers in this area. Consumer as well as enterprise both face the problems of these irrelevant information. Web Recommendation System is a fundamental tool for solving these problems. This paper discusses the details of design, implementation and efficiency of various techniques are used in the growth of the web recommendation systems. It also designs and implement dynamic recommendation system for online business using web usages mining.
Constructing Semantic Models From Words, Images, and Emojis

Abstract A number of recent models of semantics combine linguistic information, derived from text corpora, and visual information, derived from image collections, demonstrating that the resulting multimodal models are better than either of their unimodal counterparts, in accounting for behavioral data. Empirical work on semantic processing has shown that emotion also plays an important role especially in abstract concepts; however, models integrating emotion along with linguistic and visual information are lacking. Here, we first improve on visual and affective representations, derived from state‐of‐the‐art existing models, by choosing models that best fit available human semantic data and extending the number of concepts they cover. Crucially then, we assess whether adding affective representations (obtained from a neural network model designed to predict emojis from co‐occurring text) improves the model's ability to fit semantic similarity/relatedness judgments from a purely linguistic and linguistic–visual model. We find that, given specific weights assigned to the models, adding both visual and affective representations improves performance, with visual representations providing an improvement especially for more concrete words, and affective representations improving especially the fit for more abstract words.
Deep submodular network: An application to multi-document summarization

Employing deep learning makes it possible to learn high-level features from raw data, resulting in more precise models. On the other hand, submodularity makes the solution scalable and provides the means to guarantee a lower bound for its performance. In this paper, a deep submodular network (DSN) is introduced, which is a deep network meeting submodularity characteristics. DSN lets modular and submodular features to participate in constructing a tailored model that fits the best with a problem. Various properties of DSN are examined and its learning method is presented. By proving that cost function used for learning process is a convex function, it is concluded that minimization can be done in polynomial time and also, by choosing a suitable learning rate and performing enough iterations, a lower empirical error can be ensured. Finally, in order to demonstrate the applicability of DSN for real-world problems, automatic multi-document summarization is considered and a summarizer called DSNSum is introduced. Then, the performance of DSNSum is compared with the state-of-the-art summarizers based on DUC 2004 and CNN/DailyMail corpora. The experimental results show that the performance of the proposed summarizer is comparable with the state-of-the-art methods.
A Framework for Trustworthy Web Service Composition and Optimization

Recently, Web service composition and optimization have received an increasing attention from both academia and industrial community.Most current methods have not paid enough attention to the user specific trust requirement for composite services.However, Trust is an important metric to judge whether a composite service can behave as user expected.In this work, firstly, a multifactor concept of trust of composite service is defined based on the trust of component services, interface compatibility and optimal binding schemes.Secondly, a trustworthy Web service composition and optimization framework called TWSCO is proposed to guarantee the trust of composite service and efficiency of Web service composition process.The interface-matching problem among component services and user preference are considered in TWSCO, which firstly uses component services filter to remove untrusted component services.Secondly, the concrete services, based on interface similarity, are organized as clusters.Thirdly, a composite template among component services is formed at the cluster level to guarantee the trust and efficiency of composite service.finally, the best binding scheme is discovered by an optimization method based on user specific trust metrics.In the end, experiments based on real Web services are presented to illustrate the proposed framework TWSCO can effectively guarantee the user preference trust of the composite service.
Visualization of user’s attention on objects in 3D environment using only eye tracking glasses

Abstract Eye tracking technology is widely applied to detect user’s attention in a 2D field, such as web page design, package design, and shooting games. However, because our surroundings primarily consist of 3D objects, applications will be expanded if there is an effective method to obtain and display user’s 3D gaze fixation. In this research, a methodology is proposed to demonstrate the user’s 3D gaze fixation on a digital model of a scene using only a pair of eye tracking glasses. The eye tracking glasses record user’s gaze data and scene video. Thus, using image-based 3D reconstruction, a 3D model of the scene can be reconstructed from the frame images; simultaneously, the transformation matrix of each frame image can be evaluated to find 3D gaze fixation on the 3D model. In addition, a method that demonstrates multiple users’ 3D gaze fixation on the same digital model is presented to analyze gaze distinction between different subjects. With this preliminary development, this approach shows potential to be applied to a larger environment and conduct a more reliable investigation.
Semi-Supervised Learning of Processes Over Multi-Relational Graphs

Semi-supervised learning (SSL) of dynamic processes over graphs is encountered in several applications of network science. Most of the existing approaches are unable to handle graphs with multiple relations, which arise in various real-world networks. This work deals with SSL of dynamic processes over multi-relational graphs (MRGs). Towards this end, a structured dynamical model is introduced to capture the spatio-temporal nature of dynamic graph processes, and incorporate contributions from multiple relations of the graph in a probabilistic fashion. Given nodal samples over a subset of nodes and the MRG, the expectation-maximization (EM) algorithm is adapted to extrapolate nodal features over unobserved nodes, and infer the contributions from the multiple relations in the MRG simultaneously. Experiments with real data showcase the merits of the proposed approach.
Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention

A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small number of training images is needed. To tackle this problem, although finding persons who have visual attention similar to that of a target person is effective, all persons have to commonly gaze at many images. Thus, it becomes difficult and unrealistic when considering their burden. On the other hand, this paper introduces a novel adaptive image selection (AIS) scheme that focuses on the relationship between human visual attention and objects in images. AIS focuses on both a diversity of objects in images and a variance of PSMs for the objects. Specifically, AIS selects images so that selected images have various kinds of objects to maintain their diversity. Moreover, AIS guarantees the high variance of PSMs for persons since it represents the regions that many persons commonly gaze at or do not gaze at. The proposed method enables selecting similar users from a small number of images by selecting images that have high diversities and variances. This is the technical contribution of this paper. Experimental results show the effectiveness of our personalized saliency prediction including the new image selection scheme.
GFNet: A Lightweight Group Frame Network for Efficient Human Action Recognition

Human action recognition aims at assigning an action label to a well-segmented video. Recent work using two-stream or 3D convolutional neural networks achieves high recognition rates at the cost of huge computation complexity, memory footprint, and parameters. In this paper, we propose a lightweight neural network called Group Frame Network (GFNet) for human action recognition, which imposes intra-frame spatial information sparsity on spatial dimension in a simple yet effective way. Benefit from two core components, namely Group Temporal Module (GTM) and Group Spatial Module (GSM), GFNet decreases irrelevant motion inside frames and duplicate texture features among frames, which can extract the spatial-temporal information of frames at a minuscule cost. Experimental results on NTU RGB+D dataset and Varying-view RGB-D Action dataset show that our method without any pre-training strategy reaches a reasonable trade-off among computation complexity, parameters and performance, which is more cost-efficient than state-of-the-art methods.
On the Statistical and Temporal Dynamics of Sentiment Analysis

Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.
Many Views Are Not Enough: Designing for Synoptic Insights in Cultural Collections

Cultural object collections attract and delight spectators since ancient times. Yet, they also easily overwhelm visitors due to their perceptual richness and associated information. Similarly, digitized collections appear as complex, multifaceted phenomena, which can be challenging to grasp and navigate. Though visualizations can create various types of collection overviews for that matter, they do not easily assemble into a "big picture" or lead to an integrated understanding. We introduce coherence techniques to maximize connections between multiple views and apply them to the prototype PolyCube system of collection visualization: with map, set, and network visualizations it makes spatial, categorical, and relational collection aspects visible. For the essential temporal dimension, it offers four different views: superimposition, animation, juxtaposition, and space-time cube representations. A user study confirmed that better integrated visualizations support synoptic, cross-dimensional insights. An outlook is dedicated to the system's applicability within other arts and humanities data domains.
Information Maximized Variational Domain Adversarial Learning for Speaker Verification

Domain mismatch is a common problem in speaker verification. This paper proposes an information-maximized variational domain adversarial neural network (InfoVDANN) to reduce domain mismatch by incorporating an InfoVAE into domain adversarial training (DAT). DAT aims to produce speaker discriminative and domain-invariant features. The InfoVAE has two roles. First, it performs variational regularization on the learned features so that they follow a Gaussian distribution, which is essential for the standard PLDA backend. Second, it preserves mutual information between the features and the training set to extract extra speaker discriminative information. Experiments on both SRE16 and SRE18-CMN2 show that the InfoVDANN outperforms the recent VDANN, which suggests that increasing the mutual information between the latent features and input features enables the InfoVDANN to extract extra speaker information that is otherwise not possible.
14.2 A 65nm 24.7µJ/Frame 12.3mW Activation-Similarity-Aware Convolutional Neural Network Video Processor Using Hybrid Precision, Inter-Frame Data Reuse and Mixed-Bit-Width Difference-Frame Data Codec

Convolutional Neural Networks (CNNs) have become widely used in image signal processing, such as tracking, classification and post-processing. Modern CNNs use millions of weights and activations, leading to critical challenges for both computation and data transmission. Video applications, such as autopilot and surveillance cameras, have to process a large number of sequential images/frames within limited time, making the situation even worse. As shown in Fig. 14.2.1, adjacent activation frames of typical video applications are similar to each other most of the time, providing an opportunity to reduce both computing and data transmission complexity significantly.
Interpretable Machine Learning In Sustainable Edge Computing: A Case Study of Short-Term Photovoltaic Power Output Prediction

With the Internet of Things continuously penetrating into all spheres of our daily lives, the increasing use of smart devices enabled the emergence of the edge computing paradigm. To meet the needs of saving energy and reducing electricity bills for each household, solar energy is exploited by using photovoltaic (PV) panels that can be integrated into an edge computing platform based on a cost-effective scheduling scheme. However, it is still a major challenge to determine the optimal energy allocation of renewable energy due to the intermittent nature of renewable energy generation. In this paper, we propose a unified clustering-based prediction framework with two tree-based algorithms to provide short-term prediction of PV power output. We also provide the in-terpretability analysis for our approach to reveal the features that are important for the prediction. The experimental results show our proposed framework is superior to other benchmark machine learning algorithms.
Improving Proper Noun Recognition in End-To-End Asr by Customization of the Mwer Loss Criterion

Proper nouns present a challenge for end-to-end (E2E) automatic speech recognition (ASR) systems in that a particular name may appear only rarely during training, and may have a pronunciation similar to that of a more common word. Unlike conventional ASR models, E2E systems lack an explicit pronounciation model that can be specifically trained with proper noun pronounciations and a language model that can be trained on a large text-only corpus. Past work has addressed this issue by incorporating additional training data or additional models. In this paper, we instead build on recent advances in minimum word error rate (MWER) training to develop two new loss criteria that specifically emphasize proper noun recognition. Unlike past work on this problem, this method requires no new data during training or external models during inference. We see improvements ranging from 2% to 7% relative on several relevant benchmarks.
Transformer-Based Text-to-Speech with Weighted Forced Attention

This paper investigates state-of-the-art Transformer- and FastSpeech-based high-fidelity neural text-to-speech (TTS) with full-context label input for pitch accent languages. The aim is to realize faster training than conventional Tacotron-based models. Introducing phoneme durations into Tacotron-based TTS models improves both synthesis quality and stability. Therefore, a Transformer-based acoustic model with weighted forced attention obtained from phoneme durations is proposed to improve synthesis accuracy and stability, where both encoder-decoder attention and forced attention are used with a weighting factor. Furthermore, FastSpeech without a duration predictor, in which the phoneme durations are predicted by another conventional model, is also investigated. The results of experiments using a Japanese female corpus and the WaveGlow vocoder indicate that the proposed Transformer using forced attention with a weighting factor of 0.5 outperforms other models, and removing the duration predictor from FastSpeech improves synthesis quality, although the proposed weighted forced attention does not improve synthesis stability.
The complexity of human computation via a concrete model with an application to passwords

What can humans compute in their heads? We are thinking of a variety of cryptographic protocols, games like sudoku, crossword puzzles, speed chess, and so on. For example, can a person compute a function in his or her head so that an eavesdropper with a powerful computer-who sees the responses to random inputs-still cannot infer responses to new inputs? To address such questions, we propose a rigorous model of human computation and associated measures of complexity. We apply the model and measures first and foremost to the problem of 1) humanly computable password generation and then, consider related problems of 2) humanly computable "one-way functions" and 3) humanly computable "pseudorandom generators." The theory of human computability developed here plays by different rules than standard computability; the polynomial vs. exponential time divide of modern computability theory is irrelevant to human computation. In human computability, the step counts for both humans and computers must be more concrete. As an application and running example, password generation schemas are humanly computable algorithms based on private keys. Humanly computable and/or humanly usable mean, roughly speaking, that any human needing-and capable of using-passwords can if sufficiently motivated generate and memorize a secret key in less than 1 h (including all rehearsals) and can subsequently use schema plus key to transform website names (challenges) into passwords (responses) in less than 1 min. Moreover, the schemas have precisely defined measures of security against all adversaries, human and/or machine.
Unified Signal Compression Using Generative Adversarial Networks

We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals. The compressed signal is represented by a latent vector fed into a generator network which is trained to produce high quality signals that minimize a target objective function. To efficiently quantize the compressed signal, non-uniformly quantized optimal latent vectors are identified by iterative back-propagation with ADMM optimization performed for each iteration. Our experiments show that the proposed algorithm outperforms prior signal compression methods for both image and speech compression quantified in various metrics including bit rate, PSNR, and neural network based signal classification accuracy.