Reviewerly/exHarmony huggingface dataset:
Fast Discrete Cross-Modal Hashing Based on Label Relaxation and Matrix Factorization

In recent years, cross-media retrieval has drawn considerable attention due to the exponential growth of multimedia data. Many hashing approaches have been proposed for the cross-media search task. However, there are still open problems that warrant investigation. For example, most existing supervised hashing approaches employ a binary label matrix, which achieves small margins between wrong labels (0) and true labels (1). This may affect the retrieval performance by generating many false negatives and false positives. In addition, some methods adopt a relaxation scheme to solve the binary constraints, which may cause large quantization errors. There are also some discrete hashing methods that have been presented, but most of them are time-consuming. To conquer these problems, we present a label relaxation and discrete matrix factorization method (LRMF) for cross-modal retrieval. It offers a number of innovations. First of all, the proposed approach employs a novel label relaxation scheme to control the margins adaptively, which has the benefit of reducing the quantization error. Second, by virtue of the proposed discrete matrix factorization method designed to learn the binary codes, large quantization errors caused by relaxation can be avoided. The experimental results obtained on two widely-used databases demonstrate that LRMF outperforms state-of-the-art cross- media methods.
Laplacian Deep Echo State Network Optimized by Genetic Algorithm

In recent years, the applications of Deep Echo State Network (DESN) are increasing in wind speed prediction, energy consumption prediction, temperature prediction, etc. However, when the reservoir structure of the DESN is too deep, its overall stability decreases, and its generalization ability decreases. For applications where the input dimension is much lower than the dimension of the reservoir structure, we proposed a novel strategy which uses genetic algorithms to optimize the Laplacian Echo State Network (LAESN) to solve the problems caused by the deep reservoir structure. We took the chickenpox case in New York City as a case, simulated the optimized DESN, and compared it with the three models - Echo State Network (ESN), DESN and LAESN, to verify its effectiveness.
Cyber-Dependent Crime Versus Traditional Crime: Empirical Evidence for Clusters of Offenses and Related Motives

It is unknown to what extent cyber-dependent offenders are distinctly different from other offenders and to what extent they have different motives. This is addressed in this study by examining to what extent cyber-dependent offenders can be distinguished from traditional offenders and by identifying clusters of cyber-dependent and traditional offenses. In addition, it is explored which motives for offending the offenders provide and to what extent a specific cluster of crimes distinguishes itself from the other clusters by specific motives. The analyses are based on a survey among a Dutch high-risk sample of adult cyber-dependent offenders (N = 268) and traditional offenders (N = 270). The principal component analysis identified seven clusters of crimes, four clusters that include only cyber-dependent crime and three clusters that only include traditional crimes. This indicates that cyber-dependent offenders can be distinguished from traditional offenders. In addition, cyber-dependent crimes can be distinguished from traditional crimes by almost all motives. The cyber-dependent crimes are mostly committed out of intrinsic motives, i.e., committing the crime is in itself rewarding. Financial motives are almost absent for cyber-dependent crime. Differences between cyber-dependent crime clusters are mainly found in extrinsic motives, i.e., the extent to which the external consequences of committing a crime is rewarding. The results are discussed in light of the existing cybercrime literature.
Global Citation Recommendation employing Multi-view Heterogeneous Network Embedding

The enormous number of research papers on the Web motivated researchers to propose models that could assist users with personalized citation recommendations. Recently, Citation Recommendation (CR) models applying Network Representation Learning (NRL) techniques have revealed promising outcomes. Still, current NRL-based models are limited in terms of employing salient factors and relations between the objects of Multi-view Heterogeneous Networks (MHNs), hence, they failed to capture researchers' preferences. Besides, these models cannot exploit heterogeneity in the networks and hence suffer from the sparsity problems. To overcome these problems, we propose GCR-MHNE model, which employs a Multi-View Heterogeneous Network Embedding method to generate personalized recommendations. Specifically, it exploits semantic relations between papers based on citations, venue information, topical relevance, authors' information, and relevant labels to learn their vector representations. Moreover, the model captures the most influential features related to each semantic relation employing an attention mechanism. Compared to its counterparts, GCR-MHNE brings 6% and 7% improvements using the openly-available datasets in terms of Mean Average Precision and Normalized Discounted Cumulative Gain metrics, respectively. Furthermore, the proposed model outperforms its counterparts when the networks are sparse.
Artificial Intelligence-Based Digital Image Steganalysis

Recently, deep learning-based models are being extensively utilized for steganalysis. However, deep learning models suffer from overfitting and hyperparameter tuning issues. Therefore, in this paper, an efficient <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>θ</mi> </math> -nondominated sorting genetic algorithm- ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>θ</mi> </math> NSGA-) III based densely connected convolutional neural network (DCNN) model is proposed for image steganalysis. <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>θ</mi> </math> NSGA-III is utilized to tune the initial parameters of DCNN model. It can control the accuracy and f-measure of the DCNN model by utilizing them as the multiobjective fitness function. Extensive experiments are drawn on STEGRT1 dataset. Comparison of the proposed model is also drawn with the competitive steganalysis model. Performance analyses reveal that the proposed model outperforms the existing steganalysis models in terms of various performance metrics.
Doing more with less

Deep neural networks (DNNs) have grown exponentially in complexity and size over the past decade, leaving only the elite who have access to massive datacenter-based resources with the ability to develop and train such models. One of the main challenges for the long tail of researchers who might have access to only limited resources (e.g., a single multi-GPU server) is limited GPU memory capacity compared to model size. The problem is so acute that the memory requirement of training large DNN models can often exceed the aggregate capacity of all available GPUs on commodity servers; this problem only gets worse with the trend of ever-growing model sizes. Current solutions that rely on virtualizing GPU memory (by swapping to/from CPU memory) incur excessive swapping overhead. In this paper, we advocate rethinking how DNN frameworks schedule computation and move data to push the boundaries of training large models efficiently on modest multi-GPU deployments.
Attention feature matching for weakly-supervised video relocalization

Localizing the desired video clip for a given query in an untrimmed video has been a hot research topic for multimedia understanding. Recently, a new task named video relocalization, in which the query is a video clip, has been raised. Some methods have been developed for this task, however, these methods often require dense annotations of the temporal boundaries inside long videos for training. A more practical solution is the weakly-supervised approach, which only needs the matching information between the query and video.
Paracoherent answer set computation

Answer Set Programming (ASP) is a well-established paradigm for declarative programming and nonmonotonic reasoning. ASP allows for flexible modeling using rules. ASP rules induce a set of intended models called answer sets. Incoherence, the non-existence of answer sets, is therefore a feature of ASP, indicating that the rules admit no intended models. However, this feature can also be problematic in certain circumstances: errors that cause incoherence are notoriously difficult to debug, and query answering will not provide any meaningful answers for incoherent programs. Paracoherent semantics have been suggested as a remedy. They extend the classical notion of answer sets to draw meaningful conclusions also from incoherent programs. However, paracoherent semantics have essentially been inapplicable in practice, due to the lack of efficient algorithms and implementations. In this paper, this lack is addressed, and several different algorithms to compute semi-stable and semi-equilibrium models are proposed and implemented within an answer set solving framework. A key role in the framework is played by syntactic program transformations that allow for characterizing paracoherent semantics in terms of the answer sets of transformed programs. Apart from existing transformations from the literature, a novel transformation is also proposed, which provides an alternative characterization of paracoherent semantics in terms of (extended) externally supported models. Notably, the new transformation is more compact than the existing ones, and brings performance benefits. An extensive empirical performance comparison among the algorithms on benchmarks from ASP competitions and a real-world use case is given as well. It shows not only that the methods developed in this paper lead to practically effective systems, but also show a clear advantage of the methods that rely on (extended) externally supported models.
A Hybrid Approach for Automatic Extractive Summarization

In recent times, there have been many works in automatic text summarization as it has become a very intriguing topic of natural language processing. A summary should be concise, delivering all the important facts of a document. State-of-the-art extractive text summarizers use sentence ranking in various ways to extract significant summary sentences. In this paper, a hybrid approach has been introduced for single document extractive summarization. A combination of approaches such as sentence-ranking based on key-phrases and sentiment analysis has been proposed. Moreover, the work combines another approach that picks summary sentences based on their interconnection with other sentences in the text for getting a better result. Through empirical experiments, the proposed approach has been found to generate better summaries than similar existing systems.
Network Bending: Expressive Manipulation of Deep Generative Models

We introduce a new framework for manipulating and interacting with deep generative models that we call network bending. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated images. We outline this framework, demonstrating our results on state-of-the-art deep generative models trained on several image datasets. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.
Blockchain for Healthcare Sector-Analytical Review

Abstract Blockchain is a collection of records and blocks that are integrated using cryptography. Every block consists of certain levels of cryptographic hash for the previous block in the form of a timestamp and the transaction data integral to the transaction record. The blockchain systems have currently used among the various set of industrial information networks. If effectively used, it shall help the system in having more secured levels of development. In this research review, the objective is to understand the gamut of blockchain implementation and to pursue the scope in the usage of the blockchain solutions in healthcare solutions. Review of the literature and data from the case scenario analysis throws light on a certain set of constraints and vulnerabilities of using the blockchain in healthcare which needs attention. Profoundly the constraints are about the quality of nodes verifying the blocks and the anomaly access of information from the blocks. By overcoming such constraints, there will be an improvement of effectiveness with which the system gets implemented.
Chebyshev-Harmonic-Fourier-Moments and Deep CNNs for Detecting Forged Handwriting

Recently developed sophisticated image processing techniques and tools have made easier the creation of high-quality forgeries of handwritten documents including financial and property records. To detect such forgeries of handwritten documents, this paper presents a new method by exploring the combination of Chebyshev-Harmonic-Fourier-Moments (CHFM) and deep Convolutional Neural Networks (D-CNNs). Unlike existing methods work based on abrupt changes due to distortion created by forgery operation, the proposed method works based on inconsistencies and irregular changes created by forgery operations. Inspired by the special properties of CHFM, such as its reconstruction ability by removing redundant information, the proposed method explores CHFM to obtain reconstructed images for the color components of the Original, Forged Noisy and Blurred classes. Motivated by the strong discriminative power of deep CNNs, for the reconstructed images of respective color components, the proposed method used deep CNNs for forged handwriting detection. Experimental results on our dataset and benchmark datasets (namely, ACPR 2019, ICPR 2018 FCD and IMEI datasets) show that the proposed method outperforms existing methods in terms of classification rate.
Advancements in augmented reality

This paper reports at Augmented Reality (AR) as a field, where a real time integration of a 3 dimensional real environment and the virtual 3D objects. This paper explains the applications related to medical, engineering, simulation, route planning, entertainment, and military that have been discussed. Two of the greatest challenges in constructing efficient augmented reality or AR systems are registration errors and sensing errors. In this paper, authors summarize current practices to overcome these issues. A discussion of future strategies and the areas requiring more study is done. Moreover, this survey can serve as an initial point for anyone those who are interested in researching and/or using augmented reality. This is because peoples are at a stage wherein adoption of technologies based on augmented reality for enhancement of our perception and to help ourselves sense our environment in a new and an enriched way is the need of the hour.
IoT assisted Waste Collection and Management system using QR codes

With a population of around 136 crores, India is one of the largest developing nations of the world. It is also one of the largest producers of solid waste, generating around 150000 tons per day, according to the Ministry of Housing and Urban affairs. The main problem lies with the management of the generated waste efficiently. Although the government has taken up several measures to ensure proper waste management techniques, most of them have not given the desired result. IoT and smart sensors can be used to manage waste efficiently. The proposed method makes use of Quick Response (QR) codes to track and monitor the waste collection procedure. The QR codes are designed to be scanned via an android application, which verifies, stores data and alerts the user. QR codes are used as they are safe and can be printed on any surface and can be scanned easily using smartphones.
Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired by the idea of deep learning, we propose a denoising method named 3D-Parallel-RicianNet, which will combine global and local information to remove noise in MR images. Specifically, we introduce a powerful dilated convolution residual (DCR) module to expand the receptive field of the network and to avoid the loss of global features. Then, to extract more local information and reduce the computational complexity, we design the depthwise separable convolution residual (DSCR) module to learn the channel and position information in the image, which not only reduces parameters dramatically but also improves the local denoising performance. In addition, a parallel network is constructed by fusing the features extracted from each DCR module and DSCR module to improve the efficiency and reduce the complexity for training a denoising model. Finally, a reconstruction (REC) module aims to construct the clean image through the obtained noise deviation and the given noisy image. Due to the lack of ground-truth images in the real MR dataset, the performance of the proposed model was tested qualitatively and quantitatively on one simulated T1-weighted MR image dataset and then expanded to four real datasets. The experimental results show that the proposed 3D-Parallel-RicianNet network achieves performance superior to that of several state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity index, and entropy metric. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.
The learning approaches using Augmented Reality in learning environments: Meta-Analysis

With the emergence of Industrial Revolution 4.0, the educational settings are changing quickly. Augmented Reality (AR) is one of the upcoming technologies. AR enhances the real world by overlaying/augmenting the virtual/digital information over it. It provides the user with the ability to interact with the created virtual world in real space. The aim of this study is to classify the learning approaches implemented through AR technology. The technique used for the analysis is derived from systematic search of online literature databases like Taylor Francis, Web of Science, Springer, ScienceDirect and Scopus. The keywords used for the search include learning approaches, AR, AR in education, AR in learning and teaching and integration approaches. The findings of this research work highlights 4 categories of educational learning approaches that highlight AR. The approaches are experimental learning, game-based, interactive and collaborative learning. The research findings can be referred by other researchers and educators to identify the potential of AR in education and the learning approaches currently used with AR for their further research on how these approaches can be effectively and efficiently implemented in educational settings.
Evaluating MIDST, A System to Support Stigmergic Team Coordination

Data science teams working on a shared analysis face coordination problems such as dividing up the work to be done, monitoring performance and integrating the pieces. Research on distributed software development teams has raised the potential of stigmergic coordination, that is, coordination through a shared work product in place of explicit communication. The MIDST system was developed to support stigmergic coordination by making individual contributions to a shared work product visible, legible and combinable. In this paper, we present initial studies of a total of 40 student teams (24 using MIDST) that shows that teams that used MIDST did experience the intended system affordances to support their work, did seem to coordinate at least in part stigmergically and performed better on an assigned project.
Jarvis in Motion

We report on a research through design study of how our everyday objects, augmented with technologies, could contribute to disseminating people's lifestyle values in public. We developed Jarvis, a hybrid object combining a glass jar with a location-tracking system. The Jarvis endeavors to make lifestyle values visible, while sometimes hiding its values across different places. As a design research artifact, the Jarvis seeks to spark meaningful interactions as zero-waste practitioners carry it around in their daily routines in public. The data visualized through the Jarvis demonstrate how users' values are spread out locally. 18 zero-waste followers participated in a week-long deployment with a design probe followed by post-deployment interviews. Our findings highlight the role of different agencies in circulating values represented by the Jarvis. We discuss our methodological insights on hybrid objects, and how the circulation of values may inform us about the alternative roles of technologies in speaking for our lifestyle values.
An Anchor-Free Siamese Network with Multi-Template Update for Object Tracking

Siamese trackers are widely used in various fields for their advantages of balancing speed and accuracy. Compared with the anchor-based method, the anchor-free-based approach can reach faster speeds without any drop in precision. Inspired by the Siamese network and anchor-free idea, an anchor-free Siamese network (AFSN) with multi-template updates for object tracking is proposed. To improve tracking performance, a dual-fusion method is adopted in which the multi-layer features and multiple prediction results are combined respectively. The low-level feature maps are concatenated with the high-level feature maps to make full use of both spatial and semantic information. To make the results as stable as possible, the final results are obtained by combining multiple prediction results. Aiming at the template update, a high-confidence multi-template update mechanism is used. The average peak to correlation energy is used to determine whether the template should be updated. We use the anchor-free network to implement object tracking in a per-pixel manner, which computes the object category and bounding boxes directly. Experimental results indicate that the average overlap and success rate of the proposed algorithm increase by about 5% and 10%, respectively, compared to the SiamRPN++ algorithm when running on the dataset of GOT-10k (Generic Object Tracking Benchmark).
Learning from Noisy Complementary Labels with Robust Loss Functions

It has been demonstrated that large-scale labeled datasets facilitate the success of machine learning. However, collecting labeled data is often very costly and error-prone in practice. To cope with this problem, previous studies have considered the use of a complementary label, which specifies a class that an instance does not belong to and can be collected more easily than ordinary labels. However, complementary labels could also be error-prone and thus mitigating the influence of label noise is an important challenge to make complementary-label learning more useful in practice. In this paper, we derive conditions for the loss function such that the learning algorithm is not affected by noise in complementary labels. Experiments on benchmark datasets with noisy complementary labels demonstrate that the loss functions that satisfy our conditions significantly improve the classification performance.