Reviewerly/exHarmony huggingface dataset:
Open GOP Resolution Switching in HTTP Adaptive Streaming with VVC

The user experience in adaptive HTTP streaming relies on offering bitrate ladders with suitable operation points for all users and typically involves multiple resolutions. While open GOP coding structures are generally known to provide substantial coding efficiency benefit, their use in HTTP streaming has been precluded through lacking support of reference picture resampling (RPR) in AVC and HEVC. The newly emerging Versatile Video Coding (VVC) standard supports RPR, but only conversational scenarios were primarily investigated during the design of VVC. This paper aims at enabling usage of RPR in HTTP streaming scenarios through analysing the drift potential of VVC coding tools and presenting a constrained encoding method that avoids severe drift artefacts in resolution switching with open GOP coding in VVC. In typical live streaming configurations, the presented method achieves -8.7% BD-rate reduction compared to closed GOP coding while in a typical Video on Demand configuration, -1.89% BD-rate reduction is reported. The constraints penalty compared to regular open GOP coding is 0.65% BD-rate in the worst case. The presented method was integrated into the publicly available open source VVC encoder VVenC v0.3.
A comprehensive performance empirical study of the symmetric algorithms:AES, 3DES, Blowfish and Twofish

Developing software applications that provide services in all business fields such as finance, health, education, real estate and social media etc., must maintain various quality attributes, such as security, performance, availability, reliability, scalability, maintainability, usability and so on. If we talk about security for example, then we must take care of couple of properties like confidentiality, integrity, availability, non-repudiation and accountability. To achieve these targets there are different ways, one of them is using cryptographic mechanism. Cryptography has two basic types of algorithms, symmetric and asymmetric. Each of them provides encryption and decryption processes. In this paper, we conducted a performance evaluation between four of the symmetric algorithms AES, 3DES, Blowfish and Twofish. We evaluated the execution time, memory utilization and ciphertext size on the encryption and decryption processes. We performed the comparison against different files size by building a .NET application using C#. By analyzing the result, we noticed that AES has the lowest execution time in encryption and decryption processes, while Twofish has the highest execution time. In the encryption process, AES and 3DES consumes less memory than the blowfish and Twofish and they used very close amount of memory. While AES utilized less memory in the decryption. Finally, Blowfish and Twofish have the biggest ciphertext size.
A machine learning approach to predict the activity of smart home inhabitant

A smart home inhabitant performs a unique pattern or sequence of tasks repeatedly. Thus, a machine learning approach will be required to build an intelligent network of home appliances, and the algorithm should respond quickly to execute the decision. This study proposes a decision tree-based machine learning approach for predicting the activities using different appliances such as state, locations and time. A noise filter is employed to remove unwanted data and generate task sequences, and dual state properties of a home appliance are utilized to extract episodes from the sequence. An incremental decision tree approach was taken to reduce execution time. The algorithm was tested using a well-known smart home dataset from MavLab. The experimental results showed that the algorithm successfully extracted 689 predictions and their location at 90% accuracy, and the total execution time was 94 s, which is less than that of existing methods. A hardware prototype was designed using Raspberry Pi 2 B to validate the proposed prediction system. The general-purpose input-output (GPIO) interfaces of Raspberry Pi 2 B were used to communicate with the prototype testbed and showed that the algorithm successfully predicted the next activities.
Respite for SMEs: A Systematic Review of Socio-Technical Cybersecurity Metrics

Cybersecurity threats are on the rise, and small- and medium-sized enterprises (SMEs) struggle to cope with these developments. To combat threats, SMEs must first be willing and able to assess their cybersecurity posture. Cybersecurity risk assessment, generally performed with the help of metrics, provides the basis for an adequate defense. Significant challenges remain, however, especially in the complex socio-technical setting of SMEs. Seemingly basic questions, such as how to aggregate metrics and ensure solution adaptability, are still open to debate. Aggregation and adaptability are vital topics to SMEs, as they require the assimilation of metrics into an actionable advice adapted to their situation and needs. To address these issues, we systematically review socio-technical cybersecurity metric research in this paper. We analyse aggregation and adaptability considerations and investigate how current findings apply to the SME situation. To ensure that we provide valuable insights to researchers and practitioners, we integrate our results in a novel socio-technical cybersecurity framework geared towards the needs of SMEs. Our framework allowed us to determine a glaring need for intuitive, threat-based cybersecurity risk assessment approaches for the least digitally mature SMEs. In the future, we hope our framework will help to offer SMEs some deserved respite by guiding the design of suitable cybersecurity assessment solutions.
MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.
Kernel extreme learning machine based hierarchical machine learning for multi-type and concurrent fault diagnosis

The detection and identification of faults in rotary machines are of great significance to the mechanical equipment reliability especially the gearbox. Traditional machine learning algorithms suffer from low diagnosis accuracy of faults that have multiple types and exist concurrently. A novel machine learning method called hierarchical machine learning (HML) was proposed in this study to improve the faults diagnosis accuracy. The proposed algorithm consists of two layers. The first layer comprises a traditional machine learning model to identify the faults with distinguishable features and filter out these faults with indistinguishable features. The second layer model recognizes the faults filtered out by the first layer. In order to verify the effectiveness of the proposed method, the gearbox simulation experiment is carried out in the study. The simulation results validate that the proposed method outperforms other algorithms under an identical measure.
Requirement prioritization framework using <scp>case‐based</scp> reasoning: A mining‐based approach

Abstract In the current era of technology and development, component‐based software development (CBSD) has been progressively implemented. Components are used in large products for multiple users with diverse viewpoints and are highly configurable to provide higher satisfaction. Components are reused because of their similar functionality to reduce complexity and ensure the correct interaction between interfaces during product development. However, implementation of component functionalities creates complications due to improper specification, prioritization of components requirements, encapsulated functionalities, and more human interaction. Furthermore, due to configurability and the involvement of multiple stakeholders, ambiguity and semantic issues arise in the behaviour of reusable components. To overcome semantic‐based specification and prioritization components' related issues, we propose a framework that uses text mining and case‐based reasoning (CBR) techniques. Results of our empirical evaluation demonstrate that the proposed framework significantly outperforms the conventional technique.
A methodology for coffee price forecasting based on extreme learning machines

This work introduces a methodology to estimate coffee prices based on the use of Extreme Learning Machines. The process is initiated by identifying the presence of nonstationary components, like seasonality and trend. These components are withdrawn if they are found. Next, the temporal lags are selected based on the response of the Partial Autocorrelation Function filter. As predictors, we address the following models: Exponential Smoothing (ES), Autoregressive (AR) and Autoregressive Integrated and Moving Average (ARIMA) models, Multilayer Perceptron (MLP) and Extreme Learning Machines (ELMs) neural networks. The computational results based on three error metrics and two coffee types (Arabica and Robusta) showed that the neural networks, especially the ELM, can reach higher performance levels than the other models. The methodology, which presents preprocessing stages, lag selection, and use of ELM, is a novelty that contributes to the coffee prices forecasting field.
MRSCAtt: A Spatio-Channel Attention-Guided Network for Mars Rover Image Classification

As the exploration of human beings pushes deeper into the galaxy, the classification of images from space and other planets is becoming an increasingly critical task. Image classification on these planetary images can be very challenging due to differences in hue, quality, illumination, and clarity when compared to images captured on Earth. In this work, we try to bridge this gap by developing a deep learning network, MRSCAtt (Mars Rover Spatial and Channel Attention), which jointly uses spatial and channel attention to accurately classify images. We use images taken by NASA’s Curiosity rover on Mars as a dataset to show the superiority of our approach by achieving state-of-the-art results with 81.53% test set accuracy on the MSL Surface Dataset, outperforming other methods. To necessitate the use of spatial and channel attention, we perform an ablation study to show the effectiveness of each of the components. We further show robustness of our approach by validating with images taken aboard NASA’s recently-landed Perseverance rover.
ZstGAN: An adversarial approach for Unsupervised Zero-Shot Image-to-image Translation

Image-to-image translation models have shown remarkable ability on transferring images among different domains. Most of existing work follows the setting that the source domain and target domain keep the same at training and inference phases, which cannot be generalized to the scenarios for translating an image from an unseen domain to another unseen domain. In this work, we propose the Unsupervised Zero-Shot Image-to-image Translation (UZSIT) problem, which aims to learn a model that can translate samples from image domains that are not observed during training. Accordingly, we propose a framework called ZstGAN: By introducing an adversarial training scheme, ZstGAN learns to model each domain with domain-specific feature distribution that is semantically consistent on vision and attribute modalities. Then the domain-invariant features are disentangled with an shared encoder for image generation. We carry out extensive experiments on CUB and FLO datasets, and the results demonstrate the effectiveness of proposed method on UZSIT task. Moreover, ZstGAN shows significant accuracy improvements over state-of-the-art zero-shot learning methods on CUB and FLO.
Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability

The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. However, fast and reliable detection of abnormal events is still a challenging work. Here, we introduce a two-stream approach that offers an autoencoder-based structure for fast and efficient detection to facilitate anomaly detection from surveillance video without labeled abnormal events. Furthermore, we present post hoc interpretability of feature map visualization to show the process of feature learning, revealing uncertain and ambiguous decision boundaries in the video sequence. Experimental results on Avenue, UCSD Ped2, and Subway datasets show that our method can detect abnormal events well and explain the internal logic of the model at the object level.
ReCF: Exploiting Response Reasoning for Correlation Filters in Real-Time UAV Tracking

Object tracking is a fundamental task for the visual perception system on the intelligent unmanned aerial vehicle (UAV). The high efficiency of correlation filter (CF) based trackers has advanced the widespread development of online UAV object tracking. This kind of method can effectively train a filter to discriminate the target from the background. However, most CF-based methods require a fixed label function over all the previous samples, leading to over-fitting and filter degradation, especially in complex drone scenarios. To address this problem, a novel adaptive response reasoning approach is proposed for CF learning. It can leverage temporal information in filter training and significantly promote the robustness of the tracker. Specifically, the proposed response reasoning method goes beyond the standard response consistency requirement and constructs an auxiliary label of the current sample. Besides, it helps learn a generic relationship between the previous and current filters, thereby realizing self-regulated filter updating and enhancing the discriminability of the filter. Extensive experiments on four well-known challenging UAV tracking benchmarks with 278 videos sequences show that the presented method yields superior results to 40 state-of-the-art trackers with real-time performance on a single CPU, which is suitable for UAV online tracking missions.
Usage of machine learning methods for early detection of money laundering schemes

The article presents the results of applying machine learning methods to identify organizations prone to money laundering. Methods of data preprocessing were analyzed: categorical features encoding, informative feature selection. Classification methods were studied, in particular, ensemble methods of machine learning, algorithms for selecting the optimal hyperparameters, and methods for assessing the quality of the model. The most significant anti-money laundering and combating the financing of terrorism (AML/CFT) features in suspicious organizations when opening a current account were determined. The use of combinations of different methods of transformation of categorical features with the type of cross-validation used in modeling of the mentioned task was explored. The expediency of using TargetEncoder with double cross-validation was demonstrated. A model for identifying organizations prone to money laundering is trained. The best prediction quality is achieved by using a gradient boosting algorithm over decision trees. The quality of hyperparameter selection using hyperopt and optuna Python libraries was studied, and the speed of obtaining the optimal set was estimated. The model can be used to form a list of the most important indicators for early detection of organizations involved in money laundering and terrorist financing (ML/TF), as well as to develop adequate recommendations to improve the compliance control process. A software tool in Python was developed that allows to solve the tasks of early detection of organizations prone to money laundering.
Towards Unbiased and Accurate Deferral to Multiple Experts

Machine learning models are often implemented in cohort with humans in the pipeline, with the model having an option to defer to a domain expert in cases where it has low confidence in its inference. Our goal is to design mechanisms for ensuring accuracy and fairness in such prediction systems that combine machine learning model inferences and domain expert predictions. Prior work on "deferral systems" in classification settings has focused on the setting of a pipeline with a single expert and aimed to accommodate the inaccuracies and biases of this expert to simultaneously learn an inference model and a deferral system. Our work extends this framework to settings where multiple experts are available, with each expert having their own domain of expertise and biases. We propose a framework that simultaneously learns a classifier and a deferral system, with the deferral system choosing to defer to one or more human experts in cases of input where the classifier has low confidence. We test our framework on a synthetic dataset and a content moderation dataset with biased synthetic experts, and show that it significantly improves the accuracy and fairness of the final predictions, compared to the baselines. We also collect crowdsourced labels for the content moderation task to construct a real-world dataset for the evaluation of hybrid machine-human frameworks and show that our proposed framework outperforms baselines on this real-world dataset as well.
Energy-Efficient Load Balancing Technique to optimize Average response time and Data Center Processing Time in Cloud Computing Environment

Abstract Cloud infrastructure is a modern computing system in which pooled services are made available to users at various times depending on their demands. The process of distributing workload among computing system nodes is known as load balancing. Loads include things like CPU power; ram capacity, and network traffic. A well-balanced load avoids the situation when some nodes are fully loaded while others are inactive or not working. Where there are many tasks in a virtual machine (VM), these tasks are delegated to underutilize VMs in the same or a separate datacenter. Modified Round Robin and Modified Honey Bee Algorithms are proposed in this article for effective load balancing based on honey bee and Round Robin foraging activity to control load through VMs. Tasks taken from VMs that are crowded are viewed as honey bees. In the suggested method which is a circular ribbon, filled VMs are considered. In order to ensure a fast responding time and a minimum number of task migrations, the planned protocol also explores the aims of tasks in VM queues. The results of the test indicate that the quality of service has improved considerably (QoS).
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are formulated as minimax game problems that generative networks attempt to approach real data distributions by adversarial learning against discriminators which learn to distinguish generated samples from real ones, of which the intrinsic problem complexity poses challenges to performance and robustness. In this work, we aim to boost model learning from the perspective of network architectures, by incorporating recent progress on automated architecture search into GANs. Specially we propose a fully differentiable search framework, dubbed alphaGAN, where the searching process is formalized as solving a bi-level minimax optimization problem. The outer-level objective aims for seeking an optimal network architecture towards pure Nash Equilibrium conditioned on the network parameters of generators and discriminators optimized with a traditional adversarial loss within inner level. The entire optimization performs a first-order approach by alternately minimizing the two-level objective in a fully differentiable manner that enables obtaining a suitable architecture efficiently from an enormous search space. Extensive experiments on CIFAR-10 and STL-10 datasets show that our algorithm can obtain high-performing architectures only with 3-GPU hours on a single GPU in the search space comprised of approximate 2×1011 possible configurations. We further validate the method on the state-of-the-art network StyleGAN2, and push the score of Fréchet Inception Distance (FID) further, i.e., achieving 1.94 on CelebA, 2.86 on LSUN-church and 2.75 on FFHQ, with relative improvements 3% ∼ 26% over the baseline architecture. We also provide a comprehensive analysis of the behavior of the searching process and the properties of searched architectures, which would benefit further research on architectures for generative models. Codes and models are available at https://github.com/yuesongtian/AlphaGAN.
Localized Triplet Loss for Fine-grained Fashion Image Retrieval

Fashion retrieval methods aim at learning a clothing-specific embedding space where images are ranked based on their global visual similarity with a given query. How-ever, global embeddings struggle to capture localized fine-grained similarities between images, because of aggregation operations. Our work deals with this problem by learning localized representations for fashion retrieval based on local interest points of prominent visual features specified by a user. We introduce a localized triplet loss function that compares samples based on corresponding patterns. We incorporate random local perturbation on the interest point as a key regularization technique to enforce local invariance of visual representations. Due to the absence of existing fashion datasets to train on localized representations, we introduce FashionLocalTriplets, a new high-quality dataset annotated by fashion specialists that contains triplets of women’s dresses and interest points. The proposed model outperforms state-of-the-art global representations on FashionLocalTriplets.