Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs

With the emerging interest in the ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual’s behavioral pattern (e.g., physical activity, location, and mood) by using sensors embedded in smart-watches and smartphones. The multi-sensor environments also come with some challenges, such as how to fuse and combine different sources of data. In this article, we explore several methods of fusion for multi-representations of data from sensors. Furthermore, multiple representations of sensor data were generated and then fused using data-level, feature-level , and decision-level fusions . The presented methods were evaluated using three publicly available human activity recognition (HAR) datasets. The presented approaches utilize Deep Convolutional Neural Networks (CNNs). A generic architecture for fusion of different sensors is proposed. The proposed method shows promising performance, with the best results reaching an overall accuracy of 98.4% for the Context-Awareness via Wrist-Worn Motion Sensors (HANDY) dataset and 98.7% for the Wireless Sensor Data Mining (WISDM version 1.1) dataset. Both results outperform previous approaches.
Region-Level Visual Consistency Verification for Large-Scale Partial-Duplicate Image Search

Most recent large-scale image search approaches build on a bag-of-visual-words model, in which local features are quantized and then efficiently matched between images. However, the limited discriminability of local features and the BOW quantization errors cause a lot of mismatches between images, which limit search accuracy. To improve the accuracy, geometric verification is popularly adopted to identify geometrically consistent local matches for image search, but it is hard to directly use these matches to distinguish partial-duplicate images from non-partial-duplicate images. To address this issue, instead of simply identifying geometrically consistent matches, we propose a region-level visual consistency verification scheme to confirm whether there are visually consistent region (VCR) pairs between images for partial-duplicate search. Specifically, after the local feature matching, the potential VCRs are constructed via mapping the regions segmented from candidate images to a query image by utilizing the properties of the matched local features. Then, the compact gradient descriptor and convolutional neural network descriptor are extracted and matched between the potential VCRs to verify their visual consistency to determine whether they are VCRs. Moreover, two fast pruning algorithms are proposed to further improve efficiency. Extensive experiments demonstrate the proposed approach achieves higher accuracy than the state of the art and provide comparable efficiency for large-scale partial-duplicate search tasks.
Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

Abstract The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text‐based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected.
Analysis of 7-years Radon time series at Campi Flegrei area (Naples, Italy) using artificial neural network method

This paper reports the analysis of soil 222Rn data recorded over 7-years in the volcanic caldera of Campi Flegrei (Naples-Italy). The relationship between Radon activity concentration and several geophysical, geochemical and meteorological parameters, influencing the gas emissions, is estimated by the Artificial Neural Network (ANN) method. The analysis goals are: the estimation (replication) of the Radon time series from influencing parameters, the forecasting of an unknown part of it, and the search for anomalies. Results prove: (i) the effectiveness of the ANN method; (ii) Radon follow the periods of agitation of the caldera, demonstrated by the comparison with previous works using different methods.
Online learning for low-latency adaptive streaming

Achieving low-latency is paramount for live streaming scenarios, that are now-days becoming increasingly popular. In this paper, we propose a novel algorithm for bitrate adaptation in HTTP Adaptive Streaming (HAS), based on Online Convex Optimization (OCO). The proposed algorithm, named Learn2Adapt-LowLatency (L2A-LL), is shown to provide a robust adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, throughput estimation or application-specific adjustments. These properties make it very suitable for users who typically experience fast variations in channel characteristics. The proposed algorithm has been implemented in DASH-IF's reference video player (dash.js) and has been made publicly available for research purposes at [22]. Real experiments show that L2A-LL reduces latency significantly, while providing a high average streaming bit-rate, without impairing the overall Quality of Experience (QoE); a result that is independent of the channel and application scenarios. The presented optimization framework, is robust due to its design principle; its ability to learn and allows for modular QoE prioritization, while it facilitates easy adjustments to consider applications beyond live streaming and/or multiple user classes.
Customer Transaction Fraud Detection Using Xgboost Model

Customer transaction fraud detection is an important application for both the public and banks and it is becoming a heated topic in research and industries. Many data mining techniques have been utilized in financial sys-tem to save consumers millions of dollars per year. In this study, we presented a Xgboost-based transaction fraud detection model with some feature engineering and visualization. The dataset is from IEEECIS Fraud Detection Competition on Kaggle, which is a well-informed data science organization. The study indicated that xgboost based model outperformed the other three methods including Support Vector Machine, Random Forest and Logistic Regression. As to two feature selection methods, Xgboost performed better. Our best model achieved 95.2% roc auc score on leader-board and defeated other 98 percent participants.
Walking by Cycling: A Novel In-Place Locomotion User Interface for Seated Virtual Reality Experiences

We introduce VR Strider, a novel locomotion user interface (LUI) for seated virtual reality (VR) experiences, which maps cycling biomechanics of the user's legs to virtual walking movements. The core idea is to translate the motion of pedaling on a mini exercise bike to a corresponding walking animation of a virtual avatar while providing audio-based tactile feedback on virtual ground contacts. We conducted an experiment to evaluate the LUI and our novel anchor-turning rotation control method regarding task performance, spatial cognition, VR sickness, sense of presence, usability and comfort in a path-integration task. The results show that VR Strider has a significant positive effect on the participants' angular and distance estimation, sense of presence and feeling of comfort compared to other established locomotion techniques, such as teleportation and joystick-based navigation. A confirmatory study further indicates the necessity of synchronized avatar animations for virtual vehicles that rely on pedalling.
Confidentiality Support over Financial Grade Consortium Blockchain

Confidentiality is an indispensable requirement in financial applications of blockchain technology, and supporting it along with high performance and friendly programmability is technically challenging. In this paper, we present a system design called CONFIDE to support on-chain confidentiality by leveraging Trust Execution Environment (TEE). CONFIDE's secure data transmission protocol and data encryption protocol, together with a highly efficient virtual machine run in TEE, guarantee the confidentiality in the life cycle of a transaction from end to end. CONFIDE proposes a secure data model along with an application-driven secure protocol to guarantee data confidentiality and integrity. Its smart contract language extension offers users the flexibility to define complex confidentiality models. CONFIDE is implemented as a plugin module to Antfin Blockchain's proprietary platform, and can be plugged into other blockchain platforms as well with its universal interface design. Nowadays, CONFIDE is supporting millions of commercial transactions daily on consortium blockchain running financial applications including supply chain finance, ABS, commodity provenance, and cold-chain logistics.
Robust Unsupervised Cross-modal Hashing for Multimedia Retrieval

With the quick development of social websites, there are more opportunities to have different media types (such as text, image, video, etc.) describing the same topic from large-scale heterogeneous data sources. To efficiently identify the inter-media correlations for multimedia retrieval, unsupervised cross-modal hashing (UCMH) has gained increased interest due to the significant reduction in computation and storage. However, most UCMH methods assume that the data from different modalities are well paired. As a result, existing UCMH methods may not achieve satisfactory performance when partially paired data are given only. In this article, we propose a new-type of UCMH method called robust unsupervised cross-modal hashing ( RUCMH ). The major contribution lies in jointly learning modal-specific hash function, exploring the correlations among modalities with partial or even without any pairwise correspondence, and preserving the information of original features as much as possible. The learning process can be modeled via a joint minimization problem, and the corresponding optimization algorithm is presented. A series of experiments is conducted on four real-world datasets (Wiki, MIRFlickr, NUS-WIDE, and MS-COCO). The results demonstrate that RUCMH can significantly outperform the state-of-the-art unsupervised cross-modal hashing methods, especially for the partially paired case, which validates the effectiveness of RUCMH.
Ensemble Learning-Based Rate-Distortion Optimization for End-to-End Image Compression

End-to-end image compression using trained deep networks as encoding/decoding models has been developed substantially in the recent years. Previous work is limited in using a single encoding/decoding model, whereas we explore the usage of multiple encoding/decoding models as an ensemble. We propose several methods to obtain multiple models. First, we adopt the boosting strategy to train multiple networks with diversity as an ensemble. Second, we train an ensemble of multiple probability distribution models to reduce the distribution gap for efficient entropy coding. Third, we present a geometric transform-based self-ensemble method. The multiple models can be regarded as the multiple coding modes, similar to those in non-deep video coding schemes. We further adopt block-level model/mode selection at the encoder side to pursue rate-distortion optimization, where we use hierarchical block partitioning to improve the adaptation ability. Compared with single-model end-to-end compression, our proposed method improves the compression efficiency significantly, leading to 21% BD-rate reduction on the Kodak dataset, without increasing the decoding complexity. On the other hand, when keeping the same compression efficiency, our method can use much simplified decoding models, where the floating-point operations are reduced by 70%.
Image forgery detection based on statistical features of block DCT coefficients

Majority of the existing detection algorithms are able to deal with either type of forgery (splicing or copy-move). However, if we require a unified approach, we need to combine two previous works for each one of the alterations. In order to solve this problem, the authors present a new algorithm for the detection of splicing and copy-move forgery in the same instance. In this paper, a forgery detection technique is proposed which exploits the artifacts originated due to manipulations performed on JPEG encoded images. In JPEG compression technique, an image is divided into non-overlapping blocks of size 8x8 pixels and discrete cosine transform (DCT) coefficients are evaluated for each block independently. When a JPEG compressed image is tampered, there is a change in the statistical properties of AC components of block DCT coefficients. To capture this change, we propose to use standard deviation and count of non-zero DCT coefficients corresponding to each of the AC frequency components independently. The images are cropped by removing a few rows and columns from the top left corner and suggested features are evaluated for test image and its cropped version. The extracted feature vector is used with the support vector machine (SVM) for the classification of authentic and forged images. Experiments are conducted on a standard dataset of pre- and post-processed forged images CASIA vl.O and v2.0 to consolidate the theoretical concept of the proposed technique. Also, the comparative analysis is performed to showcase better detection rates compared with the state-of-the-art methods.
Patient Diet Recommendation System Using K Clique and Deep learning Classifiers

There are several systems designed for the purpose of recommending. The recommending system has gained its prominence even in the medical industry for suggesting the diets for the patient’s, medicines to be taken, treatments to be taken etc. The recommendation system mainly enhances the robustness, extends protection against the many disease and improves the quality of living of an individual. So to automatically suggest the foods based on their health conditions and the level of sugar, blood pressure, protein, fat, cholesterol, age etc. the paper puts forth k-clique embedded deep learning classifier recommendation system for suggesting the diets for the patients. The K-clique incorporated in the recommendation system in an effort of getting an improved preciseness and increasing the accuracy of the deep learning classifier (gated recurrent units). The dataset for the empirical analysis of the developed system was performed with the data set of the patients collected over the internet as well as hospitals, information’s of about 50 patients were collected with thirteen features of various disease and thousand products with eight feature set. All these features were encoded and grouped into several clusters before applying into the deep learning classifiers. The better preciseness and the accuracy observed for the developed system experimentally is compared with the machine learning techniques such as logistic regression and Naïve Bayes and other deep learning classifiers such as the MLP and RNN to demonstrate the proficiency of the K-clique deep learning classifier based recommendation system (K-DLRS)
Blockchain-assisted access for federated Smart Grid domains: Coupling and features

Industry 4.0 technological expansion and the multiple accesses to the diverse Smart Grid domains (power networks, control systems, market, customer premises) entail the need to provide efficient interconnection mechanisms with connection from anywhere, at any time and in anyhow. However, this type of requirement should not only consist in imposing interoperability solutions between entities and domains, but also in searching the way to justify and trace connections (how, when, where, who) for future governance or auditing actions. This paper, therefore, presents a three layer-based interconnection architecture and several interconnection strategies, all of them adapting the traditional policy decision and enforcement approaches together with the blockchain technology to manage reliable and secure connections among entities, processes and critical resources. With this architecture in mind, the paper also analyzes the coupling level of the blockchain technology, and explores which interconnection strategy is more suitable for Smart Grid domains and their control systems.
Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data

Graph-based semi-supervised learning has been shown to be one of the most effective classification approaches, as it can exploit connectivity patterns between labeled and unlabeled samples to improve learning performance. However, we show that existing techniques perform poorly when labeled data are severely limited. To address the problem of semi-supervised learning in the presence of severely limited labeled samples, we propose a new framework, called Shoestring, that incorporates metric learning into the paradigm of graph-based semi-supervised learning. In particular, our base model consists of a graph embedding network, followed by a metric learning network that learns a semantic metric space to represent the semantic similarity between the sparsely labeled and large numbers of unlabeled samples. Then the classification can be performed by clustering the unlabeled samples according to the learned semantic space. We empirically demonstrate Shoestring's superiority over many baselines, including graph convolutional networks, label propagation and their recent label-efficient variations (IGCN and GLP). We show that our framework achieves state-of-the-art performance for node classification in the low-data regime. In addition, we demonstrate the effectiveness of our framework on image classification tasks in the few-shot learning regime, with significant gains on miniImageNet (2.57% ~ 3.59%) and tieredImageNet (1.05% ~ 2.70%).
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision

Active learning (AL) aims to minimize labeling efforts for data-demanding deep neural networks (DNNs) by selecting the most representative data points for annotation. However, currently used methods are ill-equipped to deal with biased data. The main motivation of this paper is to consider a realistic setting for pool-based semi-supervised AL, where the unlabeled collection of train data is biased. We theoretically derive an optimal acquisition function for AL in this setting. It can be formulated as distribution shift minimization between unlabeled train data and weakly-labeled validation dataset. To implement such acquisition function, we propose a low-complexity method for feature density matching using self-supervised Fisher kernel (FK) as well as several novel pseudo-label estimators. Our FK-based method outperforms state-of-the-art methods on MNIST, SVHN, and ImageNet classification while requiring only 1/10th of processing. The conducted experiments show at least 40% drop in labeling efforts for the biased class-imbalanced data compared to existing methods.
STEFANN: Scene Text Editor Using Font Adaptive Neural Network

Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively.
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus

We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. A neural network conditioned on previously detected models guides a RANSAC estimator to different subsets of all measurements, thereby finding model instances one after another. We train our method supervised, as well as, self-supervised. For supervised training of the search strategy, we contribute a new dataset for vanishing point estimation. Leveraging this dataset, the proposed algorithm is superior with respect to other robust estimators, as well as, to designated vanishing point estimation algorithms. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
Inter-Task Association Critic for Cross-Resolution Person Re-Identification

Person images captured by unconstrained surveillance cameras often have low resolutions (LR).This causes the resolution mismatch problem when matched against the high-resolution (HR) gallery images, negatively affecting the performance of person re-identification (re-id).An effective approach is to leverage image super-resolution (SR) along with person re-id in a joint learning manner.However, this scheme is limited due to dramatically more difficult gradients backpropagation during training.In this paper, we introduce a novel model training regularisation method, called Inter-Task Association Critic (INTACT), to address this fundamental problem.Specifically, INTACT discovers the underlying association knowledge between image SR and person re-id, and leverages it as an extra learning constraint for enhancing the compatibility of SR model with person re-id in HR image space.This is realised by parameterising the association constraint which enables it to be automatically learned from the training data.Extensive experiments validate the superiority of INTACT over the state-of-the-art approaches on the cross-resolution re-id task using five standard person re-id datasets.