Reviewerly/exHarmony huggingface dataset:
Visually Linked Keywords to Support Exploratory Browsing

Academic digital library searchers often employ exploratory search strategies when faced with the complex search task of finding academic literature on a topic that is new to them. Unfortunately, the interfaces employed by academic digital libraries provide little support for exploratory browsing, which is a critical first step in exploratory search. To address this shortcoming, we have designed and implemented a novel academic digital library interface (KLink Search) with two new features: visually linked keywords and an interactive workspace. Here, we present preliminary analysis of data collected in a controlled laboratory study (n=32) comparing KLink Search to a baseline interface. Participants reported higher degrees of usefulness, ease-of-use, satisfaction, and knowledge gain after using KLink Search. Participants spent more time on the search tasks, and were able to collect sets of documents that were highly relevant to the tasks. Results from this research illustrate the value of adding lightweight visual and interactive features to academic digital library search interfaces to support exploratory browsing.
Active Machine Learning Adversarial Attack Detection in the User Feedback Process

Modern Information and Communication Technology (ICT)-based applications utilize current technological advancements for purposes of streaming data, as a way of adapting to the ever-changing technological landscape. Such efforts require providing accurate, meaningful, and trustworthy output from the streaming sensors particularly during dynamic virtual sensing. However, to ensure that the sensing ecosystem is devoid of any sensor threats or active attacks, it is paramount to implement secure real-time strategies. Fundamentally, real-time detection of adversarial attacks/instances during the User Feedback Process (UFP) is the key to forecasting potential attacks in active learning. Also, according to existing literature, there lacks a comprehensive study that has a focus on adversarial detection from an active machine learning perspective at the time of writing this paper. Therefore, the authors posit the importance of detecting adversarial attacks in active learning strategy. Attack in the context of this paper through a UFP-Threat driven model has been presented as any action that exerts an alteration to the learning system or data. To achieve this, the study employed ambient data collected from a smart environment human activity recognition from (Continuous Ambient Sensors Dataset, CASA) with fully labeled connections, where we intentionally subject the Dataset to wrong labels as a targeted/manipulative attack (by a malevolent labeler) in the UFP, with an assumption that the user-labels were connected to unique identities. While the dataset's focus is to classify tasks and predict activities, our study gives a focus on active adversarial strategies from an information security point of view. Furthermore, the strategies for modeling threats have been presented using the Meta Attack Language (MAL) compiler for purposes adversarial detection. The findings from the experiments conducted have shown that real-time adversarial identification and profiling during the UFP could significantly increase the accuracy during the learning process with a high degree of certainty and paves the way towards an automated adversarial detection and profiling approaches on the Internet of Cognitive Things (ICoT).
Learning Gradient Boosted Multi-label Classification Rules

In multi-label classification, where the evaluation of predictions is less straightforward than in single-label classification, various meaningful, though different, loss functions have been proposed. Ideally, the learning algorithm should be customizable towards a specific choice of the performance measure. Modern implementations of boosting, most prominently gradient boosted decision trees, appear to be appealing from this point of view. However, they are mostly limited to single-label classification, and hence not amenable to multi-label losses unless these are label-wise decomposable. In this work, we develop a generalization of the gradient boosting framework to multi-output problems and propose an algorithm for learning multi-label classification rules that is able to minimize decomposable as well as non-decomposable loss functions. Using the well-known Hamming loss and subset 0/1 loss as representatives, we analyze the abilities and limitations of our approach on synthetic data and evaluate its predictive performance on multi-label benchmarks.
IoT and Intelligent Wireless Sensor Network for Remote Monitoring Systems of Solar Power Stations

Nowadays, the widespread use of the Internet of Things in real life has paved the way for the emergence of systems such as Smart Homes, Smart Cities, and Smart Industries. They are called smart because of their ability to work autonomously. That is, smart systems have the ability to self-control and manage by sensing various physical influences and changes, and making decisions when necessary. Today, such systems detect physical processes and changes using special wireless sensors. In this paper was explored smart wireless sensor networks (WSN) that can be used in the monitoring systems of solar power station (SPS). Six main types of sensors (such as voltage, current, temperature and humidity, illumination, solar radiation, and dust) were obtained for this monitoring system. Based on the structure and working principle of each sensor, the method of connecting to the wireless network module was illustrated. WSN for monitoring system of SPS was built, on the AnyLogic software, a simulation was created and results were obtained on its characteristics.
A Fast Self-Correction Method for Nonlinear Sinusoidal Fringe Images in 3-D Measurement

Phase-shifting profilometry (PSP) is a method used to establish the pixel correspondence between a projector and a camera using phase-shifting fringe patterns. Due to the nonlinear intensity response of the captured images, nonlinear errors will be induced into the calculated phase and thus decrease the accuracy of 3-D measurement. In this article, we propose a fast self-correction algorithm to reduce the nonlinear phase error without the need for additional projections or precalibration. In this study, we derived an average intensity expression of the captured fringe images that contain the nonlinear response parameters. We can establish a set of equations with the known projection fringe information and unknown nonlinear parameters based on the captured fringe images at different frequencies used in the multifrequency heterodyne algorithm. Based on the equation set, the nonlinear response parameters can be solved for each pixel in the captured images, and the inverse function of the system response can thus be obtained and used to correct the actual nonlinear gray value. This pointwise correction algorithm can reduce the nonlinear phase error corresponding to different frequency fringe images without increasing the number of projections and with no excessive calculation time. Furthermore, this method can use small-pitch fringe patterns to improve the measurement accuracy while maintaining the reliability of the phase unwrapping. Experimental results show that the proposed method can eliminate the nonlinear phase error, improve phase unwrapping robustness, and achieve a high measurement quality for fast 3-D measurement through PSP.
Spherical Zero-Shot Learning

Zero-shot Learning (ZSL) is a highly non-trivial task to generalize from seen to unseen classes. In this paper, we propose spherical zero-shot learning (SZSL) to address the major challenges in ZSL. By decoupling the similarity metric in the spherical embedding space into radius and angle, our SZSL can map classes to hyperspherical surfaces of different radiuses, which greatly increases its flexibility. Specifically, we introduce the spherical alignment on angles to spread classes as uniformly as possible to alleviate the hubness problem and simultaneously preserve the inter-class semantic structure to make the alignment more reasonable. We also introduce the spherical calibration with a minimum entropy based regularizer by adopting a larger radius for unseen classes than seen classes to reduce the prediction bias. Extensive experiments on five middle-scale benchmarks and large-scale ImageNet dataset demonstrate that the proposed approach consistently achieves superior performance for the traditional and generalized settings of ZSL.
Interpretable collaborative data analysis on distributed data

This paper proposes an interpretable non-model sharing collaborative data analysis method as a federated learning system, which is an emerging technology for analyzing distributed data.Analyzing distributed data is essential in many applications, such as medicine, finance, and manufacturing, due to privacy and confidentiality concerns.In addition, interpretability of the obtained model plays an important role in the practical applications of federated learning systems.By centralizing intermediate representations, which are individually constructed by each party, the proposed method obtains an interpretable model, achieving collaborative analysis without revealing the individual data and learning models distributed between local parties.Numerical experiments indicate that the proposed method achieves better recognition performance than individual analysis and comparable performance to centralized analysis for both artificial and real-world problems.
Weighted Feature Histogram of Multi-Scale Local Patch Using Multi-Bit Binary Descriptor for Face Recognition

Most face recognition methods employ single-bit binary descriptors for face representation. The information from these methods is lost in the process of quantization from real-valued descriptors to binary descriptors, which greatly limits their robustness for face recognition. In this study, we propose a novel weighted feature histogram (WFH) method of multi-scale local patches using multi-bit binary descriptors for face recognition. First, to obtain multi-scale information of the face image, the local patches are extracted using a multi-scale local patch generation (MSLPG) method. Second, with the goal of reducing the quantization information loss of binary descriptors, a novel multi-bit local binary descriptor learning (MBLBDL) method is proposed to extract multi-bit local binary descriptors (MBLBDs). In MBLBDL, a learned mapping matrix and novel multi-bit coding rules are employed to project pixel difference vectors (PDVs) into the MBLBDs in each local patch. Finally, a novel robust weight learning (RWL) method is proposed to learn a set of robust weights for each patch to integrate the MBLBDs into the final face representation. In RWL, a codebook is first constructed by clustering MBLBDs on each local patch to extract a feature histogram. Then, considering that different parts of the face have different degrees of robustness to local changes, a set of weights is learned to concatenate the feature histograms of all local patches into the final representation of a face image. In addition, to further improve the performance for heterogeneous face recognition, a coupled WFH (C-WFH) method is proposed. C-WFH maintains the similarity of the corresponding MBLBDs and feature histograms for a pair of heterogeneous face images by means of a novel coupled feature learning (CFL) method to reduce the modality gap. A series of experiments are conducted on widely used face datasets to analyze the performance of WFH and C-WFH. Extensive experimental results show that WFH and C-WFH outperform state-of-the-art face recognition methods.
A strong reinforcement parallel implementation of k-means algorithm using message passing interface

In Data analysis process clustering is one of the standard methods, which is used in many area such as pattern recognition and image segmentation and statistics, bioinformatics etc. Clustering have several algorithms in that most well know simplest algorithm is K-means, because of its simplicity, empirical success and efficiency. This present reality applications produce tremendous volumes of data, subsequently, how to productively deal with these data in a significant mining task has been a difficult and huge issue. Likewise Message passing Interface (MPI) as a Programming model for increases the scalability, performance and execution speed. Enthused by this, MPI with strong reinforcement parallel K- means clustering algorithm is implemented in this paper. The efficiency of the strong reinforcement K means clustering algorithm is improved in parallel environment by implementing in MPI methodology. In this paper performance of the clustering the data using K- means is compared between sequentially run of K- means algorithm and parallel run of K-means algorithm in Message passing interface architecture in terms of overhead cost and execution.
Roaming Service for Electric Vehicle Charging Using Blockchain-Based Digital Identity

We present a suitable approach to address the electric vehicle charging roaming problem (e-roaming). Blockchain technologies are applied to support the identity management process of users charging their vehicles and to record energy transactions securely. At the same time, off-chain cloud-based storage is used to record the transaction details. A user wallet settled on a mobile application stores user verified credentials; a backend application in the vehicle charging station validates the user credentials to authorize the energy transaction. The current model can be applied to similar contexts where the user may be required to keep several credentials from different providers to authenticate digital transactions.
Monocular Depth Estimation With Multi-Scale Feature Fusion

Depth estimation from a single image is a crucial but challenging task for reconstructing 3D structures and inferring scene geometry. However, most existing methods fail to extract more detailed information and estimate the distant small-scale objects well. In this paper, we propose a monocular depth estimation based on multi-scale feature fusion. Specifically, to obtain input features of different scales, we first feed the input images of different scales to pre-trained residual networks with sharing weights. Then, an attention mechanism is used to learn the salient features at different scales, which can integrate detailed information at large scale feature maps and scene information at small scale feature maps. Furthermore, inspired by the dense atrous spatial pyramid pooling in semantic segmentation, we build a multi-scale feature fusion dense pyramid to further improve the ability of the feature extraction. Last, a scale-invariant error loss is used to predict depth maps in log space. We evaluate our method on several public benchmark datasets (including NYU Depth V2 and KITTI). The experiment results show that the proposed method obtains better performance than the existing methods and achieves state-of-the-art results.
Granular Multilabel Batch Active Learning With Pairwise Label Correlation

Abundant data with limited labeling are a widespread bottleneck in multilabel learning. Active learning (AL) is an effective solution to gradually enhance model robustness, however how to effectively extend instance selection criteria to multilabel case remains challenging. Considering the label specificity and label correlation, a granular batch mode-based ranking active model for the multilabel (GBRAML) is proposed. Taking a bottom-up view, three granulation operators are successively constructed to formulate three granular structures. In low-level granulation operator, auxiliary label is introduced to enhance the informativeness and representativeness of each label. The contribution of labels to the usefulness of instances is incorporated with pair-wise label correlation, and is considered in the middle-level granulation operator. The labeling priority is determined by ranking the scorings coming from high-level granulation operator. To alleviate the impact of skewed label correlation, we take a three-way strategy on fitness of representative label correlation, thus a three-way GBRAML model (TGBRAML) is devised. Extensive experiments on six multilabel benchmark demonstrate GBRAML gains 5.4&#x0025; and 210.1&#x0025; improvement on MicroF1 and Average Precision over state-of-the-art batch mode multilabel active learning. The effectiveness of three-way decisions in multilabel AL is also verified.
Achieving Privacy Preservation and Billing via Delayed Information Release

Many applications such as smart metering and location based services pose strong privacy requirements but achieving privacy protection at the client side is a non-trial problem as payment for the services must be computed by the server at the end of each billing period. In this paper, we propose a privacy preservation and billing scheme termed PPDIR based on delayed information release. PPDIR relies on a novel group signature mechanism and the asymmetric Rabin cryptosystem to protect the privacy of the clients and their requests, to achieve accountability and non-repudiation, and to shift the computational complexity to the server side. It adopts a secret token for anonymity and the token is updated for each client at the beginning of each billing period and securely released only to the server at the end of the billing period. Such a strategy can prevent the server from linking a client's requests made at different billing periods. It also prevents any adversary from linking any request to any client. Note that the server is able to figure out all requests made by a client within a billing period after receiving the delayed token, which is unavoidable for billing purpose. We prove the security properties of the group signature scheme, and analyze the security strength of PPDIR. Our study indicates that PPDIR can achieve privacy-preservation, confidentiality, non-repudiation, accountability, and other security objectives. We also evaluate the performance of our scheme in terms of communication and computational overheads.
Ordered Weighted Aggregation Networks for Video Face Recognition

Video face recognition generally includes a step where all descriptors extracted for each frame are aggregated to generate a single video face representation. The most commonly used operator for aggregation is average, which gives the same relevance to each frame. Some adaptive aggregation algorithms have been developed, but most of them rely on the use of weighted mean as aggregation operator, thus disregarding many other types of aggregation operators. In this paper, we propose a novel adaptive aggregation scheme based on ordered weighted average (OWA) operators in contrast with the mainly used weighted mean scheme. Furthermore, besides presenting the theoretical aspects of our aggregation scheme, we develop two different concrete implementations to validate its suitability for video face recognition: Ordered weighted aggregation network (OWANet) and Weighted OWANet (WOWANet). Both algorithms are based on neural networks and are trainable through gradient descent in a classic supervised learning way. We conduct extensive experiments on YouTube Faces, COX Face and the IARPA Janus Benchmark A for evaluating recognition performance on verification and identification tasks. The experimentation process shows that both proposals achieve very competitive results in accuracy with respect to the existent state-of-the-art methods, while significantly reducing space and inference time.
Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs

Emerging graph neural networks (GNNs) have extended the successes of deep learning techniques against datasets like images and texts to more complex graph-structured data. By leveraging GPU accelerators, existing frameworks combine mini-batch and sampling for effective and efficient model training on large graphs. However, this setup faces a scalability issue since loading rich vertex features from CPU to GPU through a limited bandwidth link usually dominates the training cycle. In this article, we propose PaGraph, a novel, efficient data loader that supports general and efficient sampling-based GNN training on single-server with multi-GPU. PaGraph significantly reduces the data loading time by exploiting available GPU resources to keep frequently-accessed graph data with a cache. It also embodies a lightweight yet effective caching policy that takes into account graph structural information and data access patterns of sampling-based GNN training simultaneously. Furthermore, to scale out on multiple GPUs, PaGraph develops a fast GNN-computation-aware partition algorithm to avoid cross-partition access during data-parallel training and achieves better cache efficiency. Finally, it overlaps data loading and GNN computation for further hiding loading costs. Evaluations on two representative GNN models, GCN and GraphSAGE, using two sampling methods, Neighbor and Layer-wise, show that PaGraph could eliminate the data loading time from the GNN training pipeline, and achieve up to 4.8× performance speedup over the state-of-the-art baselines. Together with preprocessing optimization, PaGraph further delivers up to 16.0× end-to-end speedup.
A Blind Color Separation Model for Faithful Palette-Based Image Recoloring

Palette-based image recoloring provides a simple yet effective way for color adjustment, which allows users to interactively manipulate the color of an image by editing a compact color palette. While remarkable progress has been made by previous methods, they have the common limitations that may produce unfaithful image recoloring results i.e., the obtained result does not respond faithfully to the palette adjustment, and tend to induce visual artifacts such as color bleeding and distortion. To address these limitations, we in this paper present a novel color separation model for palette-based recoloring. Akin to previous methods, our color separation model is built upon the assumption that color of each pixel in an image can be formulated as a linear combination of a small set of same basis colors. However, different from previous palette-based recoloring methods which typically rely on heuristic rules to build the color separation model, we experimentally reveal the underlying relationship between the color separation and the palette-based recoloring, and summarize three specialized color separation priors that allow more faithful palette-based recoloring. Based on these priors, we devise a blind color separation model that not only does not require known palette as input as done in previous methods, but also enables more effective palette-based recoloring with much less visual artifacts. Experiments on two datasets demonstrate that our method outperforms the state-of-the-art palette-based recoloring methods. In addition, we show some applications enabled by the proposed color separation model, including automatic pattern coloring generation, green screen keying and region-controllable color transfer.