Reviewerly/exHarmony huggingface dataset:
Hybrid Distortion-Based Rate-Distortion Optimization and Rate Control for H.265/HEVC

Mean squared error (MSE) is adopted as the distortion metric for the rate-distortion optimization (RDO) and rate control (RC) in most of the existing video coding standards, e.g., H.265/High-Efficiency Video Coding (HEVC) and H.264/Advanced Video Coding (AVC), because the pixel-wise MSE can represent the image fidelity efficiently. As videos are usually used for visual enjoyment, recently, researchers found that the MSE is not always consistent with perceptual quality, and proposed some perceptual quality-based video coding methods by exchanging the MSE with other perceptual quality metrics completely, like the structure similarity index (SSIM), just noticeable distortion (JND), etc. We believe that a sophisticated video encoder should guarantee both the subject quality and the data fidelity. By investigating the properties of the human visual system (HVS), hybrid distortion-based RDO and RC methods are proposed to improve subjective quality while avoiding fidelity degradation. Experimental results demonstrate that the proposed methods can improve subjective quality efficiently, while preserving the image fidelity of the reconstructed videos as much as possible.
The Roles of IT Strategies and Security Investments in Reducing Organizational Security Breaches

This research examines the joint effects of information technology (IT) strategies and security investments on organizational security breaches. We focus on two forms of IT strategies: digitalization and embeddedness in IT outsourcing networks. Our longitudinal analysis of U.S. hospitals demonstrates that IT security investments reduce security breaches in less digitalized organizations but increase security breaches for highly digitalized organizations. Investing in technical network control security systems such as anti-virus and intrusion detection systems reduces external breaches. Implementing identity and access management security systems such as biometric scanning and user authentication decreases internal breaches but increases external breaches. However, organizations’ embeddedness in IT outsourcing networks weakens the impacts of these technologies investments on external breaches but amplifies the negative relationship between identity and access management security systems and internal breaches. Our results offer an alternative understanding of organizational IT security investments and explain contrary results found in prior studies. Practical guidelines on organizational IT security strategies are discussed.
Label correlation in multi-label classification using local attribute reductions with fuzzy rough sets

To improve the efficiency and effectiveness of multi-label learning tasks, label correlation has been explored and widely applied in multi-label classification. However, the existing common way to obtain label correlation is to count the co-occurrence frequency of labels in output space. From our perspective, the feature variables of input space also contain the basic information, which can affect each label variable in output space. Obviously, the crucial features for label can efficiently reflect the internal relationship between input space and the label variable. The high consistency of the crucial features for different labels indicates that these labels are closely related. Inspired by the above intuitive motivations, the crucial features explicitly for every local label class are defined and calculated based on local attribute reductions with fuzzy rough sets. Local label correlations are naturally constructed, and then global label correlation is obtained by aggregating related local label correlations. According to different judgment parameters α and global label correlation, the labels can be transformed into several independent label-related subsets. To search for the indispensable and representative features of each label-related subset, the local-reduction-based feature selection method (LRFS-α) is designed. Comprehensive experimental results on fifteen multi-label datasets characterize the performance of our methods against other seven multi-label learning methods.
DOVER-Lap: A Method for Combining Overlap-Aware Diarization Outputs

Several advances have been made recently towards handling overlapping speech for speaker diarization. Since speech and natural language tasks often benefit from ensemble techniques, we propose an algorithm for combining outputs from such diarization systems through majority voting. Our method, DOVER-Lap, is inspired from the recently proposed DOVER algorithm, but is designed to handle overlapping segments in diarization outputs. We also modify the pair-wise incremental label mapping strategy used in DOVER, and propose an approximation algorithm based on weighted k-partite graph matching, which performs this mapping using a global cost tensor. We demonstrate the strength of our method by combining outputs from diverse systems - clustering-based, region proposal networks, and target-speaker voice activity detection - on AMI and LibriCSS datasets, where it consistently outperforms the single best system. Additionally, we show that DOVER-Lap can be used for late fusion in multichannel diarization, and compares favorably with early fusion methods like beamforming.
Full Characterization of Minimal Linear Codes as Cutting Blocking Sets

In this paper, we first study in detail the relationship between minimal linear codes and cutting blocking sets, recently introduced by Bonini and Borello, and then completely characterize minimal linear codes as cutting blocking sets. As a direct result, minimal projective codes of dimension 3 and t-fold blocking sets with t ≥ 2 in projective planes are identical objects. Some bounds on the parameters of minimal codes are derived from this characterization. Using this new link between minimal codes and blocking sets, we also present new general primary and secondary constructions of minimal linear codes. As a result, infinite families of minimal linear codes not satisfying the Aschikhmin-Barg's condition are obtained. In addition to this, open problems on the parameters and the weight distributions of some generated linear codes are presented.
Improving RNN Transducer Based ASR with Auxiliary Tasks

End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recognizers. Specifically, recurrent neural network transducer (RNN-T) has shown competitive ASR performance on various benchmarks. In this work, we examine ways in which RNN-T can achieve better ASR accuracy via performing auxiliary tasks. We propose (i) using the same auxiliary task as primary RNN-T ASR task, and (ii) performing context-dependent graphemic state prediction as in conventional hybrid modeling. In transcribing social media videos with varying training data size, we first evaluate the streaming ASR performance on three languages: Romanian, Turkish and German. We find that both proposed methods provide consistent improvements. Next, we observe that both auxiliary tasks demonstrate efficacy in learning deep transformer encoders for RNN-T criterion, thus achieving competitive results -2.0%/4.2% WER on LibriSpeech test-clean/other - as compared to prior top performing models.
Analysis of Emergency Remote Education in COVID-19 Crisis Focused on the Perception of the Teachers

This descriptive study intends to identify the satisfaction perception among the teachers of the Universidad del Valle de México (UVM) concerning the use of the Microsoft Teams platform in the transition from traditional model (face-to-face) to 100% online education [Emergency Remote Teaching (ERT)]. The proposal aims to determine the perspectives of teachers regarding the use of the Microsoft Teams platform during the crisis caused by COVID-19. UVM has 6938 full-time teachers and part-time teachers who collaborated in educational programs during January-June 2020 in the 33 campuses of UVM. And an instrument was developed and applied using finite population sampling, UVM perspective of teachers, which was distributed via Google Forms. The feasibility of the data collection instrument was determined by the Cronbach’s Alpha coefficient, with a result of 0.926. The data collection period was aligned with the first isolation period: 23 March to 20 April. The results in the perception of teacher satisfaction in the different sections of the instrument established an agreement in the answers (very satisfied or satisfied) regarding values that were higher than 60% in terms of satisfaction using the equipment. The analysis of the data collected was performed to verify the proposed hypothesis with the R version 4.0 software. A G-test was performed with the Logverosimilitude coefficient to test whether the categorical variables were independent (qualitative variables that are not defined continuously). The Krammer coefficient of association was then calculated to measure the correlation.
Dealer

Data-driven machine learning has become ubiquitous. A marketplace for machine learning models connects data owners and model buyers, and can dramatically facilitate data-driven machine learning applications. In this paper, we take a formal data marketplace perspective and propose the first en&lt;u&gt; D &lt;/u&gt;-to-end mod &lt;u&gt;e&lt;/u&gt; l m &lt;u&gt;a&lt;/u&gt; rketp &lt;u&gt;l&lt;/u&gt; ace with diff &lt;u&gt;e&lt;/u&gt; rential p &lt;u&gt;r&lt;/u&gt; ivacy ( Dealer ) towards answering the following questions: How to formulate data owners' compensation functions and model buyers' price functions? How can the broker determine prices for a set of models to maximize the revenue with arbitrage-free guarantee, and train a set of models with maximum Shapley coverage given a manufacturing budget to remain competitive ? For the former, we propose compensation function for each data owner based on Shapley value and privacy sensitivity, and price function for each model buyer based on Shapley coverage sensitivity and noise sensitivity. Both privacy sensitivity and noise sensitivity are measured by the level of differential privacy. For the latter, we formulate two optimization problems for model pricing and model training, and propose efficient dynamic programming algorithms. Experiment results on the real chess dataset and synthetic datasets justify the design of Dealer and verify the efficiency and effectiveness of the proposed algorithms.
The impact of data difficulty factors on classification of imbalanced and concept drifting data streams

Abstract Class imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition and presence of unsafe types of examples. Despite often being present in real-world data, the interactions between concept drifts and local data difficulty factors have not been investigated in concept drifting data streams yet. We thoroughly study the impact of such interactions on drifting imbalanced streams. For this purpose, we put forward a new categorization of concept drifts for class imbalanced problems. Through comprehensive experiments with synthetic and real data streams, we study the influence of concept drifts, global class imbalance, local data difficulty factors, and their combinations, on predictions of representative online classifiers. Experimental results reveal the high influence of new considered factors and their local drifts, as well as differences in existing classifiers’ reactions to such factors. Combinations of multiple factors are the most challenging for classifiers. Although existing classifiers are partially capable of coping with global class imbalance, new approaches are needed to address challenges posed by imbalanced data streams.
BCFL logging: An approach to acquire and preserve admissible digital forensics evidence in cloud ecosystem

Log files are the primary source of recording users, applications and protocols, activities in the cloud ecosystem.Cloud forensic investigators can use log evidence to ascertain when, why and how a cyber adversary or an insider compromised a system by establishing the crime scene and reconstructing how the incident occurred.However, digital evidence acquisition in a cloud ecosystem is complicated and proven difficult, even with modern forensic acquisition toolkit.The multi-tenancy, Geo-location and Service-Level Agreement have added another layer of complexity in acquiring digital log evidence from a cloud ecosystem.In order to mitigate these complexities of evidence acquisition in the cloud ecosystem, we need a framework that can forensically maintain the trustworthiness and integrity of log evidence.In this paper, we design and implement a Blockchain Cloud Forensic Logging (BCFL) framework, using a Design Science Research Methodological (DSRM) approach.BCFL operates primarily in four stages: (1) Process transaction logs using Blockchain distributed ledger technology (DLT).( 2) Use a Blockchain smart contract to maintain the integrity of logs and establish a clear chain of custody.(3) Validate all transaction logs.(4) Maintain transaction log immutability.BCFL will also enhance and strengthen compliance with the European Union (EU) General Data Protection Regulation (GDPR).The results from our single case study will demonstrate that BCFL will mitigate the challenges and complexities faced by digital forensics investigators in acquiring admissible digital evidence from the cloud ecosystem.Furthermore, an instantaneous performance monitoring of the proposed Blockchain cloud forensic logging framework was evaluated.BCFL will ensure trustworthiness, integrity, authenticity and non-repudiation of the log evidence in the cloud.
Prediction of human diseases using optimized clustering techniques

In this paper an attempt is made to study the unsupervised learning clustering algorithms such as K-Means, Agglomerative, and Fuzzy C-means Clustering methods. The Nature inspired metaheuristics algorithms such as Salp Swarm Optimization Algorithm (SSOA), Gray Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization Algorithm (MFO), BAT Optimization Algorithm (BAT), and Firefly Algorithm (FFA) are used to optimize the unsupervised algorithms for better results. Objective functions like Sum of Squared Errors and Total within-Cluster Variation are taken as objective function in the optimization algorithms. The datasets used in the paper are Heart, Liver and Appendicitis. It has been found that the K-Means have given better results compared to the other algorithms.
The symptoms, causes, and repairs of bugs inside a deep learning library

In recent years, deep learning has become a hot research topic. Although it achieves incredible positive results in some scenarios, bugs inside deep learning software can introduce disastrous consequences, especially when the software is used in safety-critical applications. To understand the bug characteristic of deep learning software, researchers have conducted several empirical studies on bugs in deep learning applications. Although these studies present useful findings, we notice that none of them analyze the bug characteristic inside a deep learning library like TensorFlow. We argue that some fundamental questions of bugs in deep learning libraries are still open. For example, what are the symptoms and the root causes of bugs inside TensorFlow, and where are they? As the underlying library of many deep learning projects, the answers to these questions are useful and important, since its bugs can have impacts on many deep learning projects. In this paper, we conduct the first empirical study to analyze the bugs inside a typical deep learning library, i.e., TensorFlow. Based on our results, we summarize 8 findings, and present our answers to 4 research questions. For example, we find that the symptoms and root causes of TensorFlow bugs are more like ordinary projects (e.g., Mozilla) than other machine learning libraries (e.g., Lucene). As another example, we find that most TensorFlow bugs reside in its interfaces (26.24%), learning algorithms (11.79%), and how to compile (8.02%), deploy (7.55%), and install (4.72%) TensorFlow across platforms.
Multigraph Transformer for Free-Hand Sketch Recognition

Learning meaningful representations of free-hand sketches remains a challenging task given the signal sparsity and the high-level abstraction of sketches. Existing techniques have focused on exploiting either the static nature of sketches with convolutional neural networks (CNNs) or the temporal sequential property with recurrent neural networks (RNNs). In this work, we propose a new representation of sketches as multiple sparsely connected graphs. We design a novel graph neural network (GNN), the multigraph transformer (MGT), for learning representations of sketches from multiple graphs, which simultaneously capture global and local geometric stroke structures as well as temporal information. We report extensive numerical experiments on a sketch recognition task to demonstrate the performance of the proposed approach. Particularly, MGT applied on 414k sketches from Google QuickDraw: 1) achieves a small recognition gap to the CNN-based performance upper bound (72.80% versus 74.22%) and infers faster than the CNN competitors and 2) outperforms all RNN-based models by a significant margin. To the best of our knowledge, this is the first work proposing to represent sketches as graphs and apply GNNs for sketch recognition. Code and trained models are available at https://github.com/PengBoXiangShang/multigraph_transformer.
Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

Abstract This article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.
Hybrid meta-heuristic algorithm based deep neural network for face recognition

Face recognition has been active research in the security domain. Human face recognition gains importance for developing a secured environment for the organization and also enhances the usage of artificial intelligence for security. Face recognition has been studied over the years for accurate recognition of complete face images. However, in the real case, the presence of occlusion and noise in the image significantly affects the performance of the recognition. Even though a lot of research has been carried out in handling the occluded and noisy image, more refinement is required to achieve high accuracy. This paper proposes a simple and efficient face recognition system with occlusion and noisy faces using the deep learning concept, as it has the advantage of handling all of it. The developed model undergoes four main steps like (a) preprocessing, (b) cascaded feature extraction, (c) optimal feature selection, and (d) recognition. Initially, the preprocessing of the face image is focused in terms of face detection by Viola-Jones algorithm. Further, a set of features termed as Local Diagonal Extrema Number Pattern (LDENP), Gradient-based directional features, and Gradient-based wavelet features are extracted for the cascaded feature extraction. As the collection of features is in a cascaded manner, it leads to providing irrelevant information of features. Hence, there is a need for optimal feature selection. The hybrid meta-heuristic concept, namely Multi-Verse with Colliding Bodies Optimization (MV-CBO), is developed with the integration of Colliding Bodies Optimization (CBO) and Multi-Verse Optimizer (MVO), and it is used for performing the optimal feature selection. Further, the optimally selected features are subjected to the optimized Deep Neural Network (DNN) for recognizing the faces, in which the proposed MV-CBO is used for optimizing the activation functions (sigmoid, tanh, Relu, ArcTan, and RRelu). The experimental findings on diverse datasets with occlusion and noises prove that the extensive experiments on several benchmark databases prove the ability of the proposed model over the existing face recognition approaches.
Using a Fully Expressive Avatar to Collaborate in Virtual Reality: Evaluation of Task Performance, Presence, and Attraction

Avatar-mediated collaboration in virtual environments is becoming more and more prevalent. However, current consumer systems are not suited to fully replicate real-world nonverbal communication. We present a novel avatar system for collaboration in virtual reality, which supports high levels of nonverbal expression by tracking behavior such as body movement, hand gesture, and facial expression. The system was built using camera tracking technology only. Therefore, in contrast to many other high-level tracking systems, it does not require users to wear additional trackers on their bodies. We compared our highly expressive system with a consumer setup extended with two body-worn trackers in a dyadic study. We investigated users’ performance, such as completion time and accuracy, as well as the presence and interpersonal attraction in a virtual charades game using an asymmetric control scheme. The results show that participants interacting with highly expressive avatars felt more social presence and attraction and exhibited better task performance than those interacting with partners represented using low-expressive avatars. Hence, we conclude that virtual reality avatar systems benefit from a higher level of nonverbal expressiveness, which can be achieved without additional body-worn trackers.